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Securing Secrets: A Guide To Implementing Secrets Management in DevSecOps Pipelines

By Josephine Eskaline Joyce

CORE

Introduction to Secrets Management In the world of DevSecOps, where speed, agility, and security are paramount, managing secrets effectively is crucial. Secrets, such as passwords, API keys, tokens, and certificates, are sensitive pieces of information that, if exposed, can lead to severe security breaches. To mitigate these risks, organizations are turning to secret management solutions. These solutions help securely store, access, and manage secrets throughout the software development lifecycle, ensuring they are protected from unauthorized access and misuse. This article aims to provide an in-depth overview of secrets management in DevSecOps, covering key concepts, common challenges, best practices, and available tools. Security Risks in Secrets Management The lack of implementing secrets management poses several challenges. Primarily, your organization might already have numerous secrets stored across the codebase. Apart from the ongoing risk of exposure, keeping secrets within your code promotes other insecure practices such as reusing secrets, employing weak passwords, and neglecting to rotate or revoke secrets due to the extensive code modifications that would be needed. Here below are some of the risks highlighting the potential risks of improper secrets management: Data Breaches If secrets are not properly managed, they can be exposed, leading to unauthorized access and potential data breaches. Example Scenario A Software-as-a-Service (SaaS) company uses a popular CI/CD platform to automate its software development and deployment processes. As part of their DevSecOps practices, they store sensitive credentials, such as API keys and database passwords, in a secrets management tool integrated with their pipelines. Issue Unfortunately, the CI/CD platform they use experiences a security vulnerability that allows attackers to gain unauthorized access to the secrets management tool's API. This vulnerability goes undetected by the company's security monitoring systems. Consequence Attackers exploit the vulnerability and gain access to the secrets stored in the management tool. With these credentials, they are able to access the company's production systems and databases. They exfiltrate sensitive customer data, including personally identifiable information (PII) and financial records. Impact The data breach leads to significant financial losses for the company due to regulatory fines, legal fees, and loss of customer trust. Additionally, the company's reputation is tarnished, leading to a decrease in customer retention and potential business partnerships. Preventive Measures To prevent such data breaches, the company could have implemented the following preventive measures: Regularly auditing and monitoring access to the secrets management tool to detect unauthorized access. Implementing multi-factor authentication (MFA) for accessing the secrets management tool. Ensuring that the secrets management tool is regularly patched and updated to address any security vulnerabilities. Limiting access to secrets based on the principle of least privilege, ensuring that only authorized users and systems have access to sensitive credentials. Implementing strong encryption for storing secrets to mitigate the impact of unauthorized access. Conducting regular security assessments and penetration testing to identify and address potential security vulnerabilities in the CI/CD platform and associated tools. Credential Theft Attackers may steal secrets, such as API keys or passwords, to gain unauthorized access to systems or resources. Example Scenario A fintech startup uses a popular CI/CD platform to automate its software development and deployment processes. They store sensitive credentials, such as database passwords and API keys, in a secrets management tool integrated with their pipelines. Issue An attacker gains access to the company's internal network by exploiting a vulnerability in an outdated web server. Once inside the network, the attacker uses a variety of techniques, such as phishing and social engineering, to gain access to a developer's workstation. Consequence The attacker discovers that the developer has stored plaintext files containing sensitive credentials, including database passwords and API keys, on their desktop. The developer had mistakenly saved these files for convenience and had not securely stored them in the secrets management tool. Impact With access to the sensitive credentials, the attacker gains unauthorized access to the company's databases and other systems. They exfiltrate sensitive customer data, including financial records and personal information, leading to regulatory fines and damage to the company's reputation. Preventive Measures To prevent such credential theft incidents, the fintech startup could have implemented the following preventive measures: Educating developers and employees about the importance of securely storing credentials and the risks of leaving them in plaintext files. Implementing strict access controls and auditing mechanisms for accessing and managing secrets in the secrets management tool. Using encryption to store sensitive credentials in the secrets management tool, ensures that even if credentials are stolen, they cannot be easily used without decryption keys. Regularly rotating credentials and monitoring for unusual or unauthorized access patterns to detect potential credential theft incidents early. Misconfiguration Improperly configured secrets management systems can lead to accidental exposure of secrets. Example Scenario A healthcare organization uses a popular CI/CD platform to automate its software development and deployment processes. They store sensitive credentials, such as database passwords and API keys, in a secrets management tool integrated with their pipelines. Issue A developer inadvertently misconfigures the permissions on the secrets management tool, allowing unintended access to sensitive credentials. The misconfiguration occurs when the developer sets overly permissive access controls, granting access to a broader group of users than intended. Consequence An attacker discovers the misconfigured access controls and gains unauthorized access to the secrets management tool. With access to sensitive credentials, the attacker can now access the healthcare organization's databases and other systems, potentially leading to data breaches and privacy violations. Impact The healthcare organization suffers reputational damage and financial losses due to the data breach. They may also face regulatory fines for failing to protect sensitive information. Preventive Measures To prevent such misconfiguration incidents, the healthcare organization could have implemented the following preventive measures: Implementing least privilege access controls to ensure that only authorized users and systems have access to sensitive credentials. Regularly auditing and monitoring access to the secrets management tool to detect and remediate misconfigurations. Implementing automated checks and policies to enforce proper access controls and configurations for secrets management. Providing training and guidance to developers and administrators on best practices for securely configuring and managing access to secrets. Compliance Violations Failure to properly manage secrets can lead to violations of regulations such as GDPR, HIPAA, or PCI DSS. Example Scenario A financial services company uses a popular CI/CD platform to automate their software development and deployment processes. They store sensitive credentials, such as encryption keys and API tokens, in a secrets management tool integrated with their pipelines. Issue The financial services company fails to adhere to regulatory requirements for managing and protecting sensitive information. Specifically, they do not implement proper encryption for storing sensitive credentials and do not maintain proper access controls for managing secrets. Consequence Regulatory authorities conduct an audit of the company's security practices and discover compliance violations related to secrets management. The company is found to be non-compliant with regulations such as PCI DSS (Payment Card Industry Data Security Standard) and GDPR (General Data Protection Regulation). Impact The financial services company faces significant financial penalties for non-compliance with regulatory requirements. Additionally, the company's reputation is damaged, leading to a loss of customer trust and potential legal consequences. Preventive Measures To prevent such compliance violations, the financial services company could have implemented the following preventive measures: Implementing encryption for storing sensitive credentials in the secrets management tool to ensure compliance with data protection regulations. Implementing strict access controls and auditing mechanisms for managing and accessing secrets to prevent unauthorized access. Conducting regular compliance audits and assessments to identify and address any non-compliance issues related to secrets management. Lack of Accountability Without proper auditing and monitoring, it can be difficult to track who accessed or modified secrets, leading to a lack of accountability. Example Scenario A technology company uses a popular CI/CD platform to automate its software development and deployment processes. They store sensitive credentials, such as API keys and database passwords, in a secrets management tool integrated with their pipelines. Issue The company does not establish clear ownership and accountability for managing and protecting secrets. There is no designated individual or team responsible for ensuring that proper security practices are followed when storing and accessing secrets. Consequence Due to the lack of accountability, there is no oversight or monitoring of access to sensitive credentials. As a result, developers and administrators have unrestricted access to secrets, increasing the risk of unauthorized access and data breaches. Impact The lack of accountability leads to a data breach where sensitive credentials are exposed. The company faces financial losses due to regulatory fines, legal fees, and loss of customer trust. Additionally, the company's reputation is damaged, leading to a decrease in customer retention and potential business partnerships. Preventive Measures To prevent such lack of accountability incidents, the technology company could have implemented the following preventive measures: Designating a specific individual or team responsible for managing and protecting secrets, including implementing and enforcing security policies and procedures. Implementing access controls and auditing mechanisms to monitor and track access to secrets, ensuring that only authorized users have access. Providing regular training and awareness programs for employees on the importance of secrets management and security best practices. Conducting regular security audits and assessments to identify and address any gaps in secrets management practices. Operational Disruption If secrets are not available when needed, it can disrupt the operation of DevSecOps pipelines and applications. Example Scenario A financial institution uses a popular CI/CD platform to automate its software development and deployment processes. They store sensitive credentials, such as encryption keys and API tokens, in a secrets management tool integrated with their pipelines. Issue During a routine update to the secrets management tool, a misconfiguration occurs that causes the tool to become unresponsive. As a result, developers are unable to access the sensitive credentials needed to deploy new applications and services. Consequence The operational disruption leads to a delay in deploying critical updates and features, impacting the financial institution's ability to serve its customers effectively. The IT team is forced to troubleshoot the issue, leading to downtime and increased operational costs. Impact The operational disruption results in financial losses due to lost productivity and potential revenue. Additionally, the financial institution's reputation is damaged, leading to a loss of customer trust and potential business partnerships. Preventive Measures To prevent such operational disruptions, the financial institution could have implemented the following preventive measures: Implementing automated backups and disaster recovery procedures for the secrets management tool to quickly restore service in case of a failure. Conducting regular testing and monitoring of the secrets management tool to identify and address any performance issues or misconfigurations. Implementing a rollback plan to quickly revert to a previous version of the secrets management tool in case of a failed update or configuration change. Establishing clear communication channels and escalation procedures to quickly notify stakeholders and IT teams in case of operational disruption. Dependency on Third-Party Services Using third-party secrets management services can introduce dependencies and potential risks if the service becomes unavailable or compromised. Example Scenario A software development company uses a popular CI/CD platform to automate its software development and deployment processes. They rely on a third-party secrets management tool to store sensitive credentials, such as API keys and database passwords, used in their pipelines. Issue The third-party secrets management tool experiences a service outage due to a cyber attack on the service provider's infrastructure. As a result, the software development company is unable to access the sensitive credentials needed to deploy new applications and services. Consequence The dependency on the third-party secrets management tool leads to a delay in deploying critical updates and features, impacting the software development company's ability to deliver software on time. The IT team is forced to find alternative ways to manage and store sensitive credentials temporarily. Impact The dependency on the third-party secrets management tool results in financial losses due to lost productivity and potential revenue. Additionally, the software development company's reputation is damaged, leading to a loss of customer trust and potential business partnerships. Preventive Measures To prevent such dependencies on third-party services, the software development company could have implemented the following preventive measures: Implementing a backup plan for storing and managing sensitive credentials locally in case of a service outage or disruption. Diversifying the use of secrets management tools by using multiple tools or providers to reduce the impact of a single service outage. Conducting regular reviews and assessments of third-party service providers to ensure they meet security and reliability requirements. Implementing a contingency plan to quickly switch to an alternative secrets management tool or provider in case of a service outage or disruption. Insider Threats Malicious insiders may abuse their access to secrets for personal gain or to harm the organization. Example Scenario A technology company uses a popular CI/CD platform to automate their software development and deployment processes. They store sensitive credentials, such as API keys and database passwords, in a secrets management tool integrated with their pipelines. Issue An employee with privileged access to the secrets management tool decides to leave the company and maliciously steals sensitive credentials before leaving. The employee had legitimate access to the secrets management tool as part of their job responsibilities but chose to abuse that access for personal gain. Consequence The insider threat leads to the theft of sensitive credentials, which are then used by the former employee to gain unauthorized access to the company's systems and data. This unauthorized access can lead to data breaches, financial losses, and damage to the company's reputation. Impact The insider threat results in financial losses due to potential data breaches and the need to mitigate the impact of the stolen credentials. Additionally, the company's reputation is damaged, leading to a loss of customer trust and potential legal consequences. Preventive Measures To prevent insider threats involving secrets management, the technology company could have implemented the following preventive measures: Implementing strict access controls and least privilege principles to limit the access of employees to sensitive credentials based on their job responsibilities. Conducting regular audits and monitoring of access to the secrets management tool to detect and prevent unauthorized access. Providing regular training and awareness programs for employees on the importance of data security and the risks of insider threats. Implementing behavioral analytics and anomaly detection mechanisms to identify and respond to suspicious behavior or activities involving sensitive credentials. Best Practices for Secrets Management Here are some best practices for secrets management in DevSecOps pipelines: Use a dedicated secrets management tool: Utilize a specialized tool or service designed for securely storing and managing secrets. Encrypt secrets at rest and in transit: Ensure that secrets are encrypted both when stored and when transmitted over the network. Use strong access controls: Implement strict access controls to limit who can access secrets and what they can do with them. Regularly rotate secrets: Regularly rotate secrets (e.g., passwords, API keys) to minimize the impact of potential compromise. Avoid hardcoding secrets: Never hardcode secrets in your code or configuration files. Use environment variables or a secrets management tool instead. Use environment-specific secrets: Use different secrets for different environments (e.g., development, staging, production) to minimize the impact of a compromised secret. Monitor and audit access: Monitor and audit access to secrets to detect and respond to unauthorized access attempts. Automate secrets retrieval: Automate the retrieval of secrets in your CI/CD pipelines to reduce manual intervention and the risk of exposure. Regularly review and update policies: Regularly review and update your secrets management policies and procedures to ensure they are up-to-date and effective. Educate and train employees: Educate and train employees on the importance of secrets management and best practices for handling secrets securely. Use-Cases of Secrets Management For Different Tools Here are the common use cases for different tools of secrets management: IBM Cloud Secrets Manager Securely storing and managing API keys Managing database credentials Storing encryption keys Managing certificates Integrating with CI/CD pipelines Compliance and audit requirements by providing centralized management and auditing of secrets usage. Ability to dynamically generate and rotate secrets HashiCorp Vault Centralized secrets management for distributed systems Dynamic secrets generation and management Encryption and access controls for secrets Secrets rotation for various types of secrets AWS Secrets Manager Securely store and manage AWS credentials Securely store and manage other types of secrets used in AWS services Integration with AWS services for seamless access to secrets Automatic secrets rotation for supported AWS services Azure Key Vault Centralized secrets management for Azure applications Securely store and manage secrets, keys, and certificates Encryption and access policies for secrets Automated secrets rotation for keys, secrets, and certificates CyberArk Conjur Secrets management and privileged access management Secrets retrieval via REST API for integration with CI/CD pipelines Secrets versioning and access controls Automated secrets rotation using rotation policies and scheduled tasks Google Cloud Secret Manager Centralized secrets management for Google Cloud applications Securely store and manage secrets, API keys, and certificates Encryption at rest and in transit for secrets Automated and manual secrets rotation with integration with Google Cloud Functions These tools cater to different cloud environments and offer various features for securely managing and rotating secrets based on specific requirements and use cases. Implement Secrets Management in DevSecOps Pipelines Understanding CI/CD in DevSecOps CI/CD in DevSecOps involves automating the build, test, and deployment processes while integrating security practices throughout the pipeline to deliver secure and high-quality software rapidly. Continuous Integration (CI) CI is the practice of automatically building and testing code changes whenever a developer commits code to the version control system (e.g., Git). The goal is to quickly detect and fix integration errors. Continuous Delivery (CD) CD extends CI by automating the process of deploying code changes to testing, staging, and production environments. With CD, every code change that passes the automated tests can potentially be deployed to production. Continuous Deployment (CD) CD goes one step further than continuous delivery by automatically deploying every code change that passes the automated tests to production. This requires a high level of automation and confidence in the automated tests. Continuous Compliance (CC) CC refers to the practice of integrating compliance checks and controls into the automated CI/CD pipeline. It ensures that software deployments comply with relevant regulations, standards, and internal policies throughout the development lifecycle. DevSecOps DevSecOps integrates security practices into the CI/CD pipeline, ensuring that security is built into the software development process from the beginning. This includes performing security testing (e.g., static code analysis, dynamic application security testing) as part of the pipeline and managing secrets securely. The following picture depicts the DevSecOps lifecycles: Picture courtesy Implement Secrets Management Into DevSecOps Pipelines Implementing secrets management into DevSecOps pipelines involves securely handling and storing sensitive information such as API keys, passwords, and certificates. Here's a step-by-step guide to implementing secrets management in DevSecOps pipelines: Select a Secrets Management Solution Choose a secrets management tool that aligns with your organization's security requirements and integrates well with your existing DevSecOps tools and workflows. Identify Secrets Identify the secrets that need to be managed, such as database credentials, API keys, encryption keys, and certificates. Store Secrets Securely Use the selected secrets management tool to securely store secrets. Ensure that secrets are encrypted at rest and in transit and that access controls are in place to restrict who can access them. Integrate Secrets Management into CI/CD Pipelines Update your CI/CD pipeline scripts and configurations to integrate with the secrets management tool. Use the tool's APIs or SDKs to retrieve secrets securely during the pipeline execution. Implement Access Controls Implement strict access controls to ensure that only authorized users and systems can access secrets. Use role-based access control (RBAC) to manage permissions. Rotate Secrets Regularly Regularly rotate secrets to minimize the impact of potential compromise. Automate the rotation process as much as possible to ensure consistency and security. Monitor and Audit Access Monitor and audit access to secrets to detect and respond to unauthorized access attempts. Use logging and monitoring tools to track access and usage. Best Practices for Secrets Management Into DevSecOps Pipelines Implementing secrets management in DevSecOps pipelines requires careful consideration to ensure security and efficiency. Here are some best practices: Use a secrets management tool: Utilize a dedicated to store and manage secrets securely. Encrypt secrets: Encrypt secrets both at rest and in transit to protect them from unauthorized access. Avoid hardcoding secrets: Never hardcode secrets in your code or configuration files. Use environment variables or secrets management tools to inject secrets into your CI/CD pipelines. Rotate secrets: Implement a secrets rotation policy to regularly rotate secrets, such as passwords and API keys. Automate the rotation process wherever possible to reduce the risk of human error. Implement access controls: Use role-based access controls (RBAC) to restrict access to secrets based on the principle of least privilege. Monitor and audit access: Enable logging and monitoring to track access to secrets and detect any unauthorized access attempts. Automate secrets retrieval: Automate the retrieval of secrets in your CI/CD pipelines to reduce manual intervention and improve security. Use secrets injection: Use tools or libraries that support secrets injection (e.g., Kubernetes secrets, Docker secrets) to securely inject secrets into your application during deployment. Conclusion Secrets management is a critical aspect of DevSecOps that cannot be overlooked. By implementing best practices such as using dedicated secrets management tools, encrypting secrets, and implementing access controls, organizations can significantly enhance the security of their software development and deployment pipelines. Effective secrets management not only protects sensitive information but also helps in maintaining compliance with regulatory requirements. As DevSecOps continues to evolve, it is essential for organizations to prioritize secrets management as a fundamental part of their security strategy. More

Ansible Beyond Automation

By Vidyasagar (Sarath Chandra) Machupalli

CORE

Ansible is one of the fastest-growing Infrastructure as Code (IaC) and automation tools in the world. Many of us use Ansible for Day 1 and Day 2 operations. One of the best analogies to understand the phases/stages/operations is defined on RedHat's website: "Imagine you're moving into a house. If Day 1 operations are moving into the house (installation), Day 2 operations are the 'housekeeping' stage of a software’s life cycle." Simply put, in a software lifecycle: Day 0: Design/planning phase - This phase involves preparation, initial planning, brainstorming, and preparing for the project. Typical activities in this phase are defining the scope, gathering requirements, assembling the development team, and setting up the development environments. For example, the team discusses the CI/CD platform to integrate the project with, the strategy for project management, etc. Day 1: Development/deployment phase - This phase marks the actual development activities such as coding, building features, and implementation based on the requirements gathered in the planning phase. Additionally, testing will begin to ensure early detection of issues (in development lingo, "bugs"). Day 2: Maintenance phase - This phase in which your project/software goes live and you keep a tap on the health of the project. You may need to patch or update the software and file feature requests/issues based on user feedback for your development team to work on. This is the phase where monitoring and logging (observability) play a crucial role. Ansible is an open-source tool written in Python and uses YAML to define the desired state of configuration. Ansible is used for configuration management, application deployment, and orchestration. It simplifies the process of managing and deploying software across multiple servers, making it one of the essential tools for system administrators, developers, and IT operations teams. With AI, generating Ansible code has become simpler and more efficient. Check out the following article to learn how Ansible is bringing AI tools to your Integrated Development Environment: "Automation, Ansible, AI." RedHat Ansible Lightspeed with IBM Watsonx code assistant At its core, Ansible employs a simple, agentless architecture, relying on SSH to connect to remote servers and execute tasks. This eliminates the need for installing any additional software or agents on target machines, resulting in a lightweight and efficient automation solution. Key Features of Ansible Here is a list of key features that Ansible offers: Infrastructure as Code (IaC) Ansible allows you to define your infrastructure and configuration requirements in code, enabling you to version control, share, and replicate environments with ease. For example, say you plan to move your on-premises application to a cloud platform. Instead of provisioning the cloud services and installing the dependencies manually, you can define the required cloud services and dependencies for your application like compute, storage, networking, security, etc., in a configuration file. That desired state is taken care of by Ansible as an Infrastructure as Code tool. In this way, setting up your development, test, staging, and production environments will easily avoid repetition. Playbooks Ansible playbooks are written in YAML format and define a series of tasks to be executed on remote hosts. Playbooks offer a clear, human-readable way to describe complex automation workflows. Using playbooks, you define the required dependencies and desired state for your application. Modules Ansible provides a vast collection of modules for managing various aspects of systems, networks, cloud services, and applications. Modules are idempotent, meaning they ensure that the desired state of the system is achieved regardless of its current state. For example, ansible.bultin.command is a module that helps you to execute commands on a remote machine. You can either use modules that are built in, like dnf, yum, etc., as part of Ansible Core, or you can develop your own modules in Ansible. To further understand the Ansible modules, check out this topic on RedHat. Inventory Management Ansible uses an inventory file to define the hosts it manages. This inventory can be static or dynamic, allowing for flexible configuration management across different environments. An inventory file (.ini or .yaml) is a list of hosts or nodes on which you install, configure, or set up a software, add a user, or change the permissions of a folder, etc. Refer to how to build an inventory for best practices. Roles Roles in Ansible provide a way to organize and reuse tasks, variables, and handlers. They promote code reusability and help maintain clean and modular playbooks. You can group tasks that are repetitive as a role to reuse or share with others. One good example is pinging a remote server, you can move the tasks, variables, etc., under a role to reuse. Below is an example of a role directory structure with eight main standard directories. You will learn about a tool to generate this defined structure in the next section of this article. Shell roles/ common/ # this hierarchy represents a "role" tasks/ # main.yml # <-- tasks file can include smaller files if warranted handlers/ # main.yml # <-- handlers file templates/ # <-- files for use with the template resource ntp.conf.j2 # <------- templates end in .j2 files/ # bar.txt # <-- files for use with the copy resource foo.sh # <-- script files for use with the script resource vars/ # main.yml # <-- variables associated with this role defaults/ # main.yml # <-- default lower priority variables for this role meta/ # main.yml # <-- role dependencies library/ # roles can also include custom modules module_utils/ # roles can also include custom module_utils lookup_plugins/ # or other types of plugins, like lookup in this case webtier/ # same kind of structure as "common" was above, done for the webtier role monitoring/ # "" fooapp/ Beyond Automation Ansible finds applications in several areas. Configuration management: Ansible simplifies the management of configuration files, packages, services, and users across diverse IT infrastructures. Application deployment: Ansible streamlines the deployment of applications by automating tasks such as software installation, configuration, and version control. Continuous Integration/Continuous Deployment (CI/CD): Ansible integrates seamlessly with CI/CD pipelines, enabling automated testing, deployment, and rollback of applications. Orchestration: Ansible orchestrates complex workflows involving multiple servers, networks, and cloud services, ensuring seamless coordination and execution of tasks. Security automation: Ansible helps enforce security policies, perform security audits, and automate compliance checks across IT environments. Cloud provisioning: Ansible's cloud modules facilitate the provisioning and management of cloud resources on platforms like IBM Cloud, AWS, Azure, Google Cloud, and OpenStack. The list is not exhaustive, so only a subset of applications is included above. Ansible can act as a security compliance manager by enforcing security policies and compliance standards across infrastructure and applications through patch management, configuration hardening, and vulnerability remediation. Additionally, Ansible can assist in setting up monitoring and logging, automating disaster recovery procedures (backup and restore processes, failovers, etc.,), and integrating with a wide range of tools and services, such as version control systems, issue trackers, ticketing systems, and configuration databases, to create end-to-end automation workflows. Tool and Project Ecosystem Ansible provides a wide range of tools and programs like Ansible-lint, Molecule for testing Ansible plays and roles, yamllint, etc. Here are additional tools that are not mentioned in the Ansible docs: Ansible Generator: Creates the necessary folder/directory structure; comes in handy when you create Ansible roles AWX: Provides a web-based user interface, REST API, and task engine built on top of Ansible; Comes with an awx-operator if you are planning to set up on a container orchestration platform like RedHat OpenShift Ansible VS Code extension by Red Hat: Syntax highlighting, validation, auto-completion, auto-closing Jinja expressions ("{{ my_variable }") etc. The Ansible ecosystem is very wide. This article gives you just a glimpse of the huge set of tools and frameworks. You can find the projects in the Ansible ecosystem on Ansible docs. Challenges With Ansible Every tool or product comes with its own challenges. Learning curve: One of the major challenges with Ansible is the learning curve. Mastering the features and best practices can be time-consuming, especially for users new to infrastructure automation or configuration. Complexity: Initially, understanding the terminology, folder structure, and hierarchy challenges the user. Terms like inventory, modules, plugins, tasks, playbooks, etc., are hard to understand in the beginning. As the number of nodes/hosts increases, the complexity of managing the playbooks, and orchestrating increases. Troubleshooting and error handling: For beginners, troubleshooting errors and debugging playbooks can be challenging. Especially, understanding error messages and identifying the root cause of failures requires familiarity with Ansible's syntax and modules, etc. Conclusion In this article, you learned that Ansible as an open-source tool can be used not only for automation but also for configuration, deployment, and security enablement. You also understood the features, and challenges and learned about the tools Ansible and the community offers. Ansible will become your go-to Infrastructure as Code tool once you pass the initial learning curve. To overcome the initial complexity, here's a GitHub repository with Ansible YAML code snippets to start with. Happy learning. If you like this article, please like and share it with your network. More

Beyond the Resume: Practical Interview Techniques for Hiring Great DevSecOps Engineers

By Roman Burdiuzha

Strategic Insights Into Azure DevOps: Balancing Advantages and Challenges

By Harshavardhan Nerella

DevOps vs. DataOps vs. MLOps Vs. AIOps: Comparison of All "Ops"

By Ravi Kiran Mallidi

CORE

Optimizing Azure DevOps Pipelines With AI and Continuous Integration

Overview of Azure DevOps Azure DevOps is a set of tools and services for software development that covers everything from planning and coding to testing and deployment. Developed by Microsoft and based in the cloud Azure DevOps facilitates collaboration and project management efficiency offering features tailored to developers and operations teams alike. This platform enables organizations to deliver top-notch software products by simplifying workflows and promoting teamwork among teams. Figure courtesy from Microsoft An essential aspect of Azure DevOps is Azure Repositories, which offer robust source control management. Developers can work together on projects, manage code versions, and maintain a record of changes. With support for branching and merging strategies teams can experiment with features without jeopardizing the stability of the codebase. Another critical element within Azure DevOps is Azure Boards, which provides a suite of tools for project management and tracking work items. Teams can create tasks, user stories and bugs using boards and backlogs to prioritize work and plan sprints efficiently to keep projects on schedule. By integrating methodologies, like Scrum and Kanban, teams can adopt industry practices while continuously enhancing their processes. Azure Pipelines serves as the engine for Continuous Integration and Continuous Deployment (CI/CD) in Azure DevOps. It automates tasks like builds, tests, and deployments making the release process smoother and reducing errors. Developers can set up pipeline configurations using YAML files to define the steps and environments involved in building and deploying applications. Azure Pipelines is versatile supporting a variety of programming languages, platforms, and cloud services making it suitable for project needs. Azure Artifacts functions as a package management service that enables teams to manage dependencies across projects. Developers can create, share, and use packages to ensure consistency in their development processes. The service supports package formats such as NuGet, npm, Maven, and PyPI to cater to project requirements. Azure Test Plans provide a suite of testing tools for testing and exploratory testing activities. Teams can effectively manage test cases, execute tests, and track bugs within the Azure DevOps environment. This integration ensures that thorough testing is seamlessly integrated into the development lifecycle to help identify issues. Moreover, Azure DevOps integrates seamlessly, with third-party tools and services to expand its capabilities and empower teams to tailor their workflows based on requirements. Some common tools integrated with Azure DevOps include Jenkins, GitHub, Docker, and Kubernetes. This versatility enables teams to make the most of their existing tools while taking advantage of Azure DevOps's strong features. One of the benefits of Azure DevOps is its ability to scale up or down based on project size and complexity. As a cloud-based solution, it can cater to projects ranging from development teams to enterprise endeavors. This scalability allows teams to focus on their development tasks without having to worry about managing infrastructure resources. Moreover, Azure DevOps provides analytics and reporting functionalities that offer insights into project performance and progress, for teams. Dashboards and reports are useful for teams to monitor metrics, like the success rates of building and deploying completion of work items and code coverage. This data-focused approach enables teams to make informed decisions and continually enhance their methods. Simply put Azure DevOps is a platform that supports the software development cycle. With features for source control, project management, CI/CD, package management, and testing Azure DevOps simplifies. Encourages teamwork among groups. Its ability to integrate with tools and services coupled with its emphasis on security and scalability positions it as a robust option for organizations seeking to enhance their software development processes. Understanding Continuous Integration (CI) Continuous Integration (CI) is a development practice that focuses on automating the process of combining code modifications from contributors into a shared repository reliably. This approach helps in the detection and resolution of integration issues during the development phase leading to stable software releases and a smoother development journey. CI plays a role in software development practices and is commonly linked with Continuous Delivery (CD) or Continuous Deployment to establish a seamless transition from code creation to production deployment. Essentially CI entails the merging of code changes made by team members into a central repository followed by automated building and testing processes. This enables developers to promptly identify and resolve integration conflicts and problems thereby minimizing the chances of introducing bugs or other issues into the codebase. Through the integration of changes, teams can uphold a level of code quality and uniformity. A standard CI workflow comprises stages. Initially, developers commit their code alterations to a version control system (VCS) like Git. The CI server keeps an eye on the VCS repository for any commits. Triggers an automated build once it detects changes. Throughout the construction phase, the server compiles the code. Executes a series of automated tests, which include unit tests, integration tests, and other forms of testing, like static code analysis or security scans. If all goes well with the build and tests the alterations are deemed integrated. The build is labeled as successful. In case any issues arise, such as test failures or build glitches the CI server promptly notifies developers for resolution. This quick feedback loop stands out as an advantage of CI enabling teams to catch problems and prevent development delays. CI also fosters collaboration and communication among team members. With frequent code integrations happening developers can regularly. Discuss each other's work. This practice promotes peer review culture and ongoing improvement efforts helping teams uphold standards of code quality and adhere to practices. A significant benefit of CI lies in its ability to thwart the integration hell" scenario where substantial changes are infrequently merged leading to an integration process that consumes time. By integrating changes, through CI practices teams can mitigate risks effectively and maintain a consistent development pace Another crucial aspect of Continuous Integration (CI) involves utilizing automation tools to oversee the build and testing procedures. CI servers, like Jenkins, GitLab CI/CD, and Azure DevOps Pipelines offer automation functionalities that streamline workflows and maintain consistency across builds. These tools can be customized to execute tasks, such as code compilation, test execution, and report generation based on the team's needs. In summary, Continuous Integration plays a role in software development by promoting high standards of code quality and efficiency. By integrating code changes, automating builds and tests, and providing feedback CI helps teams identify issues early on and avoid integration difficulties. This enables teams to deliver software products while maintaining a smooth development workflow. Establishing an Azure DevOps Pipeline With Continuous Integration Initiating a New Azure DevOps Project Sign in to Azure DevOps. Click on "Create New Project". Specify a project name. Choose the desired visibility setting (public or private). Create the project. Configuring Source Code Repositories Within your project navigate to "Repositories" to establish your source code repository. Create a repository. Replicate an existing one from an external origin. Setting up Build Processes Navigate to the "Pipelines" section in your Azure DevOps project. Select "New Pipeline". Indicate the source of your code (Azure Repos, GitHub, etc.). Opt for a pipeline template. Craft a new one from scratch. Outline the steps for constructing your application (compiling code and executing tests). Save your settings. Initiate the pipeline execution. Setting up Deployment Workflows Navigate to the "Pipelines" section. Choose "Releases." Select "New release pipeline". Pick a source pipeline (build pipeline) for your deployment. Outline the stages for your deployment workflow (e.g., Development, Staging, Production). Include tasks for deploying, configuring, and any follow-up steps after deployment. Save. Execute the workflow. Benefits of Optimizing Azure DevOps Pipeline Optimizing Azure DevOps pipelines brings advantages that can enhance the effectiveness and quality of software development and deployment processes. By streamlining workflows and promoting collaboration organizations can achieve more software delivery. Here are some key advantages of optimizing Azure DevOps pipelines, Quicker Feedback Loops Optimized pipelines offer feedback on code modifications through automated builds and tests enabling developers to promptly detect and address issues. Rapid feedback aids in reducing the time needed to resolve bugs and enhancing code quality. Enhanced Code Quality Automated testing, encompassing unit, integration, and end-to-end tests ensures that code alterations do not introduce problems or setbacks. Incorporating AI-driven code quality assessment tools can help spot issues like code irregularities, security susceptibilities, and undesirable patterns. Improved Developer Efficiency By automating tasks, like builds, tests, and deployments developers can concentrate on crafting top-notch code and creating features. Efficient pipelines diminish involvement. Decrease the likelihood of human errors. Boosted Dependability Consistent and automated testing guarantees that the software stays stable and functional throughout the development cycle. Automated deployments can be authenticated against predefined acceptance criteria to lessen deployment complications. Efficient Use of Resources Improving workflows can help manage the distribution and utilization of resources reducing resource consumption and expenses. Utilizing features, like processing and data caching can accelerate the building and deployment procedures while minimizing infrastructure costs. Scalability and Adaptability Azure DevOps pipelines can be easily expanded to support projects of sizes and complexities catering to both development teams and large corporate ventures. The platform offers support for programming languages, frameworks, and cloud services providing flexibility in tool selection and customization options. Enhanced Collaboration and Communication Functions such as requests, code evaluations, and threaded discussions facilitate teamwork among members by enabling collaboration on code modifications. Optimized workflows promote a culture of enhancement and knowledge exchange among team members. Improved Monitoring and Analysis Azure DevOps provides tools for monitoring performance metrics and project advancement offering insights into pipeline efficiency. Interactive dashboards and detailed reports help teams monitor indicators such as success rates, in building/deployment processes, test coverage levels, and task completion progress. Continuous Enhancement Streamlined workflows empower teams to iterate rapidly while continuously enhancing their development practices. By pinpointing areas needing improvement and bottlenecks, teams can enhance their workflows. Embrace strategies. Embracing DevOps Principles Azure DevOps pipelines facilitate the adoption of DevOps principles, like Infrastructure as Code (IaC) automated testing and continuous delivery. These principles play a role in streamlining development processes to become more agile and efficient. To sum up, streamlining Azure DevOps pipelines brings about advantages that lead to more dependable and superior software releases. Through the utilization of automation, AI-driven tools, and best practices teams can elevate their development procedures, for increased productivity and effectiveness. AI in Azure DevOps Pipelines With Example AI can bring significant enhancements to Azure DevOps pipelines, making them more efficient, reliable, and productive. By leveraging AI, you can improve code quality, optimize testing, automate various tasks, and gain insights from data analysis. One of the useful ways you can use AI in Azure DevOps pipelines is to enable automatic issue detection and resolution. Let's look into it, Automated Issue Detection and Resolution AI can automatically detect and even resolve common issues in the pipeline, such as build failures or flaky tests. Using AI to detect and resolve common issues in the pipeline, such as build failures or flaky tests, can improve the stability and reliability of your development workflow. Here's an example that demonstrates how you can use AI in an Azure DevOps pipeline to detect and resolve common issues: 1. Integrate AI-Based Monitoring and Insights Start by integrating AI-based monitoring and insights into your pipeline. This will enable you to gather data on pipeline performance and identify potential issues. Use Azure monitor: Integrate Azure Monitor with your pipeline to collect logs, metrics, and traces from your builds and tests. Configure AI-based anomaly detection: Use AI-based anomaly detection to monitor the pipeline for unusual patterns or deviations from expected performance. 2. Detecting Pipeline Issues With AI AI can be used to monitor the pipeline in real-time and detect common issues such as build failures or flaky tests. Analyze build logs: Use AI to analyze build logs and identify patterns that indicate build failures or flaky tests. Monitor test results: AI can monitor test results for inconsistencies, such as tests that pass intermittently (flaky tests). 3. Resolving Common Issues Automatically Once AI detects an issue, you can configure automated actions to resolve the problem. Automatic retry: If a build failure is detected, configure the pipeline to automatically retry the build to see if the issue persists. Flaky test management: If flaky tests are detected, AI can tag them for further investigation and potentially quarantine them to prevent them from impacting the pipeline. Rollbacks: If an issue occurs during deployment, AI can automatically trigger a rollback to the previous stable version. 4. Example Pipeline Configuration Here is an example Azure DevOps pipeline configuration (azure-pipelines.yml) that demonstrates how you might integrate with Azure OpenAI to "Generate code comments." YAML trigger: - main pr: - main pool: vmImage: 'ubuntu-latest' jobs: - job: GenerateCodeComments displayName: 'Generate Code Comments with Azure OpenAI' steps: - checkout: self displayName: 'Checkout Code' - task: AzureCLI@2 displayName: 'Generate Code and Comments with Azure OpenAI' inputs: azureSubscription: 'Your Azure Subscription' scriptLocation: 'inlineScript' inlineScript: | # Set the endpoint and API key for Azure OpenAI Service OPENAI_ENDPOINT="https://YOUR_AZURE_OPENAI_ENDPOINT.azure.com" OPENAI_API_KEY="YOUR_AZURE_OPENAI_API_KEY" # Prepare the prompt for code completion and comment generation # This example uses a placeholder. In practice, dynamically extract relevant code snippets or provide context. PROMPT="Extracted code snippet for analysis" # Make a REST API call to Azure OpenAI Service response=$(curl -X POST "$OPENAI_ENDPOINT/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ --data "{ \"model\": \"code-davinci-002\", \"prompt\": \"$PROMPT\", \"temperature\": 0.7, \"max_tokens\": 150, \"top_p\": 1.0, \"frequency_penalty\": 0.0, \"presence_penalty\": 0.0 }") echo "Generated code and comments:" echo $response # The response will contain the generated code completions and comments. # Consider parsing this response and integrating suggestions into the codebase manually or through automated scripts. # Optional: Add steps for reviewing or applying the generated suggestions # - script: echo "Review and integrate suggestions" # displayName: 'Review Suggestions' - Key Points Trigger and PR: This pipeline is triggered by commits to the main branch and pull requests targeting the main branch, ensuring that code comments and suggestions are generated for the most current and relevant changes. AzureCLI task: The core of this pipeline is the AzureCLI task, which makes a REST API call to the Azure OpenAI Service, passing a code snippet (the PROMPT) and receiving AI-generated code comments and suggestions. Dynamic prompt extraction: The example uses a static prompt. In a real-world scenario, you would dynamically extract relevant code snippets from your repository to use as prompts. This might involve additional scripting or tools to analyze your codebase and select meaningful snippets for comment generation. Review and integration: The optional step at the end hints at a manual or automated process for reviewing and integrating the AI-generated suggestions into your codebase. The specifics of this step would depend on your team's workflow and the tools you use for code review and integration. 5. Configure AI-Based Analysis Custom AI model: Use Azure Cognitive Services or another AI model to analyze build logs and test results for patterns indicative of common issues. Trigger actions: Based on the analysis results, trigger automated actions such as retrying builds, quarantining flaky tests, or rolling back deployments. 6. Review and Improve Monitor and adjust: Continuously monitor the AI-based analysis and automated actions to ensure they are effective in resolving issues. Feedback loop: Incorporate feedback from the AI analysis into your development process to continuously improve the pipeline's reliability and stability. By leveraging AI to detect and resolve common issues in the pipeline, you can minimize downtime, reduce manual intervention, and create a more robust and efficient development process. Conclusion By optimizing Azure DevOps pipelines with AI and Continuous Integration you can greatly boost the development process by enhancing efficiency, code quality, and reliability. This guide offers instructions on configuring and optimizing Azure DevOps pipelines with AI and CI.

By Naga Santhosh Reddy Vootukuri

CORE

Configuration as Code: Everything To Know

With modern tools and QAOps methodologies, infrastructure as code and configuration as code is taking development practices in an operational context to a whole new level. As a result, you get a much more rigid, streamlined process that’s much faster, better automated, and drastically reduces errors, not to mention giving you very consistent output. This is what configuration as code provides us. An application’s codebase and server deployment configuration are usually separated during software development and deployment. The Ops team often creates the configuration settings and tools necessary to build and deploy your app across various server instances and environments. Using configuration as code entails treating configuration settings similarly to your application code. For configuration settings, you should take advantage of version control. What Is Configuration as Code? An approach to managing your software called “configuration as code” advocates for configuration settings (such as environmental settings, resources provisioning, etc.) to be defined in code. This entails committing your software configuration settings to a version control repository and handling them the same way you would the rest of your code. This contrasts with having your configuration located elsewhere other than the repository or possibly needing to create and customize the configuration for each deployment. As a result, it becomes way easier to synchronize configuration changes across different deployments or instances. You can publish server change updates to the repository like any other commit, which can subsequently be picked up and sent to the server like any other update, saving you from having to configure server changes or use another out-of-code solution manually. Infrastructure as Code vs. Configuration as Code The approach of treating infrastructure as though it were software is known as infrastructure as code (IaC). You can write code to specify how your infrastructure should seem if you consider it another application in your software stack. Once tested, you may use that description to create or destroy infrastructure automatically. IaC and CaC both automate the provisioning and configuration of your software, but they do so in various ways. In infrastructure as a code, you codify your infrastructure so a machine can manage it. Before deploying your system, you build scripts that specify how you want it to be configured and how it should look. IaC is frequently used to automate the deployment and configuration of both physical and virtual servers. Before deploying an application, CaC requires you to model its configuration. When you implement new software configurations, your application configuration settings are updated without requiring manual involvement. CaC applies to containers, microservices, and other application types. Merge requests, CI/CD and IaC are essential GitOps techniques. Git is the only source of truth in GitOps, a method of controlling declarative infrastructure. Infrastructure updates are a crucial part of the software integration and delivery process with GitOps, and you can incorporate them into the same CI/CD pipeline. This integration simplifies config updates. Simply creating and pushing the configuration modifications to the source control repository is all that is required from a developer. Before making changes to the underlying infrastructure, the code in this repository is tested using CI/CD technologies. Why Use Configuration as Code? Teams can benefit from implementing configuration as code in several ways. Scalability Handling configuration changes as code, like IaC, enables teams to create, update, and maintain config files from a single centralized location while leveraging a consistent deployment approach. For instance, you require configuration files for each storage option if you are developing USB devices. You may create thousands of configurations by combining these files with the required software. To handle these variations, you need a robust, centralized source control that can be accessed from different levels in your CI/CD pipeline. Standardization When the configuration is written like source code, you can use your development best practices, such as linting and security scanning. Before they are committed, config files must be reviewed and tested to guarantee that modifications adhere to your team’s standards. Your configurations can be maintained stable and consistent via a complicated microservices architecture. Services function more effectively together when a set process is in place. Traceability Setting up configuration as code requires version control. You require a robust system that can conveniently save and track changes to your configuration and code files. This could improve the level of quality of your release. You can locate its source if a bug still slips through and rapidly identify/fix an issue by comparing the versioned configuration files. Increased Productivity You may streamline your build cycle by turning configurations into managed code. Both IT and end users are more productive as a result. Your administrators may incorporate everything into a release or build from a single version control system. Developers are confident in the accuracy of their changes because every component of your workflow has been tested in concert. When To Use Configuration as Code? Configuration as code is used to manage settings for packages and components. This works across a wide range of industries. During the development of an app, configurations might be utilized to support several operating systems. By maintaining configuration as code, you may track hundreds or even thousands of hardware schematics and testing information for embedded development. How Teams Implement Configuration as Code You must decide how to save the configuration files you create or refactor in your code in your version control system. Teams can accomplish this in various ways: Put configuration files and code in the same repository (monorepo). Keep configuration files and code together based on your needs. component-based development and microservices. Keep configurations and code in separate repositories. Monorepo Strategy Your workflow may be simpler if all your files are in one repository. However, if you treat configuration files as source code, any change to a setting can result in a fresh build. This might not be necessary and might make your team work more slowly. Not every config update demands a build. Your system’s administrators would have to configure it to enable the merging of changes to configuration files. They might then be deployed to one of your pre-production environments to do further testing. Because everything is code, it might be challenging to distinguish between configuration files and source code when performing an audit. Establishing a naming convention that is uniform across teams is crucial. Microservices/Component-Based Development Teams often separate their code into several repos for various reasons. According to this architecture, configuration files are kept and versioned alongside the particular microservice or component. Even though you might get a similar problem with trigger builds, it might be simpler to handle. Collaborate with your DevOps teams if you plan to version config files with their microservice or component. Plan how configuration changes will be distributed. Separate Repos for Configuration Files Whatever method you use to save your source code, some teams prefer to keep their configuration files in a separate repository. Although it sounds like an excellent idea, this is rarely viable. Even the most complicated projects may contain fewer than a thousand configuration files. As a result, they would occupy a relatively small space within a repository. The setup of your build pipeline would require time from your administrators. You might wish to consider alternative solutions, even if this paradigm can be useful for audits, rollbacks, and reviews. Config as Code: Use Cases What does “Config as Code” mean in practice? It can be put into effect in several different ways, not all of which are appropriate for every organization. See if the broad strokes below meet your particular needs: Making use of unique configuration source control repositories. Creating a custom build and deployment procedure. Establishing test environments with a focus on configuration. Making sure there are procedures for approval and quality control. Secrets management within configurations. Creating Test Environments for Configuration Maybe setting up a complete testing environment for application code is not necessary for a simple configuration modification. A company can save time and money by limiting the scope of a test environment to the requirements of the configuration deployment process. Additionally, this might imply that various changes can co-occur. During the testing of a configuration change, application developers can test their code. You improve environmental management and operation efficiency with this capacity for parallel testing. Conclusion Your development team can reap significant advantages by incorporating configuration as code into your process. Applying updates and ensuring that everything works as intended is made simpler by automating the deployment of configurations across environments. Changes are simple to manage and track because it uses a single repository. While enhancing the development and deployment of code, configuration as code is a valuable tool for managing and controlling complex infrastructure and pipelines. As a result, you have the visibility and control you need to speed up development without compromising the security of your deployments.

By Hamid Akhtar

ArgoCD Rollout vs. Flagger: Setup Guide and Analysis

With the rise of high-frequency application deployment, CI/CD has been adopted by the modern software development industry. But many organizations are still looking for a solution that will give them more control over the delivery of their applications such as the Canary deployment method or even Blue Green. Called Progressive Delivery, this process will give organizations the ability to run multiple versions of their application and reduce the risk of pushing a bad release. In this post, we will focus on Canary deployment as there’s a high demand for organizations to run testing in production with real users and real traffic which Blue Green deployment cannot do. ArgoCD vs. Flagger: Overview A Canary deployment will be triggered by ArgoCD Rollout and Flagger if one of these changes ais applied: Deployment PodSpec (container images, commands, ports, env, resources, etc) ConfigMaps mounted as volumes or mapped to environment variables Secrets mounted as volumes or mapped to environment variables Why Not Use Kubernetes RollingUpdate? Kubernetes offers by default their RollingUpdate deployment strategy, but it can be limited due to: No fine-grained control over the speed of a new release, by default Kubernetes will wait for the new pod to get into a ready state and that’s it. Can’t manage traffic flow, without traffic split, it is impossible to send a percentage of the traffic to a newer release and adjust its percentage. No ability to verify external metrics such as Prometheus custom metrics to verify the status of a new release. Unable to automatically abort or rollback the update What Is ArgoCD Rollout? Just a year after ArgoCD creation, in 2019 the group behind the popular ArgoCD decided to overcome these limitations from Kubernetes by creating ArgoCD Rollout as a Kubernetes Controller used to achieve Canary, Blue Green, Canary analysis, experimentation, and progressive delivery features to Kubernetes with the most popular service mesh and ingress controllers. What Is Flagger? Created in 2018 by the FluxCD community, FluxCD has been growing massively since its creation and offers Flagger as one of its GitOps components to deal with progressive delivery on Kubernetes. Flagger helps developers solidify their production releases by applying canary, A/B testing, and Blue Green deployment strategies. It has direction integration with service mesh such as Istio and Linkerd but also ingress controllers like NGINX or even Traefik. How ArgoCD Rollout and Flagger Work With Istio If you are using Istio as a service mesh to deal with traffic management and want to use Canary as a deployment strategy: ArgoCD Rollout will transform your Kubernetes Deployment as a ReplicaSet. To start, you would need to create the Istio DestinationRule and Virtual Service but also the two Kubernetes Services (stable and canary) The next step would be creating your rollout, ArgoCD Rollout will manage the Virtual Service to match with the current desired canary weight and your DestionationRule that will contain the label for the canary ReplicaSet. Example: YAML apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: reviews-rollout namespace: default spec: replicas: 1 selector: matchLabels: app: reviews version: stable template: metadata: labels: app: reviews version: stable service.istio.io/canonical-revision: stable spec: serviceAccountName: bookinfo-reviews containers: - name: reviews image: docker.io/istio/examples-bookinfo-reviews-v1:1.18.0 imagePullPolicy: IfNotPresent env: - name: LOG_DIR value: "/tmp/logs" ports: - containerPort: 9080 volumeMounts: - name: tmp mountPath: /tmp - name: wlp-output mountPath: /opt/ibm/wlp/output securityContext: runAsUser: 1000 volumes: - name: wlp-output emptyDir: {} - name: tmp emptyDir: {} strategy: canary: canaryService: reviews-canary stableService: reviews-stable trafficRouting: istio: virtualService: name: reviews destinationRule: name: reviews canarySubsetName: canary stableSubsetName: stable steps: - setWeight: 20 - pause: {} # pause indefinitely - setWeight: 40 - pause: {duration: 10s} - setWeight: 60 - pause: {duration: 10s} - setWeight: 80 - pause: {duration: 10s} Here’s a documentation link for the Istio ArgoCD Rollout integration. Flagger relies on a k8s custom resource called Canary, example below: YAML apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: reviews namespace: default spec: # deployment reference targetRef: apiVersion: apps/v1 kind: Deployment name: reviews # the maximum time in seconds for the canary deployment # to make progress before it is rollback (default 600s) progressDeadlineSeconds: 60 service: # service port number port: 9080 analysis: # schedule interval (default 60s) interval: 15s # max number of failed metric checks before rollback threshold: 5 # max traffic percentage routed to canary # percentage (0-100) maxWeight: 50 # canary increment step # percentage (0-100) stepWeight: 10 As seen on L11, you don’t have to define your deployment but can call its name so the k8s deployment is managed outside of the Canary custom resource. Once you apply this, Flagger will automatically create the Canary resources: # generated deployment.apps/reviews-primary service/reviews service/reviews-canary service/reviews-primary destinationrule.networking.istio.io/reviews-canary destinationrule.networking.istio.io/reviews-primary virtualservice.networking.istio.io/reviews As you can see, it created the Istio Destinationrule and Virtual service to achieve traffic management for canary deployment. How Does ArgoCD Rollout Compare to Flagger? Both solutions support the same service mesh and share a very similar analysis process but there are a few features that can make the difference in choosing your progressive delivery tool for Kubernetes ArgoCD Rollout Flagger + - Great UI/dashboard to manage releases - ArgoCD dashboard (not Rollout dashboard) can interact with ArgoCD Rollout to approve promotions. - Kubectl plugin which makes it easy to query via a CLI rollout status. - Automatically creates the Kubernetes Services, Istio DestinationRule, and Virtual Service. - Load tester can run advanced testing scenarios. - - ArgoCD Rollout needs you to create Kubernetes Services, Istio DestinationRules, and Vertical Services manually. - No authentication or RBAC for the Rollout dashboard. - CLI only, no UI/dashboard. - Logs can lack information, in addition to being difficult to visualize. - No Kubectl plugin to easily fetch deployment information. - Documentation may not be as detailed as ArgoCD Rollout. Conclusion Both solutions are backed up by strong communities so there’s not a bad option that really stands out. You may already be using FluxCD. In this case, Flagger makes sense as an option to achieve progressive delivery and the same goes for ArgoCD and ArgoCD Rollout We hope this helps you get an idea of how ArgoCD Rollout and Flagger work with Canary deployments and Istio, in addition to giving you a general overview of the two solutions.

By Chase Bolt

How I Finally Got All My CI/CD in One Place: Getting My CI/CD Act Together With Heroku Flow

The Heroku team has long been an advocate of CI/CD. Their platform integrates with many third-party solutions like GitLab CI/CD or GitHub Actions. In a previous article, I demonstrated how you can configure your Heroku app with GitLab CI/CD to automatically deploy your app to production. In a follow-up article, I walked you through a slightly more nuanced setup involving both a staging environment and a production environment. But if you want to go all in on Heroku, you can use a series of solutions called Heroku Flow to configure all your CI/CD without any third parties. Heroku Flow brings together Heroku pipelines, Heroku CI, Heroku review apps, a GitHub integration, and a release phase. In this article, I’ll show you how to set this up for your own projects. Getting Started Before we begin, if you’d like to follow along, you’ll need a Heroku account and a GitHub account. You can create a Heroku account here, and you can create a GitHub account here. The demo app shown in this article is deployed to Heroku, and the code is hosted on GitHub. Running Our App Locally You can run the app locally by forking the repo in GitHub, installing dependencies, and running the start command. In your terminal, do the following after forking the repo: $ cd heroku-flow-demo $ npm install $ npm start After starting the app, visit http://localhost:5001/ in your browser, and you’ll see the app running locally: Demo app Creating Our Heroku Pipeline Now that we have the app running locally, let’s get it deployed to Heroku so that it can be accessed anywhere, not just on your machine. We’ll create a Heroku pipeline that includes a staging app and a production app. To create a new Heroku pipeline, navigate to your Heroku dashboard, click the “New” button in the top-right corner of the screen, and then choose “Create new pipeline” from the menu. Create new pipeline In the dialog that appears, give your pipeline a name, choose an owner (yourself), and connect your GitHub repo. If this is your first time connecting your GitHub account to Heroku, a second popup will appear in which you can confirm giving Heroku access to GitHub. After connecting to GitHub, click “Create pipeline” to finish the process. Configure your pipeline With that, you’ve created a Heroku pipeline. Well done! Newly created pipeline Creating Our Staging and Production Apps Most engineering organizations use at least two environments: a staging environment and a production environment. The staging environment is where code is deployed for acceptance testing and any additional QA. Code in the staging environment is then promoted to the production environment to be released to actual users. Let’s add a staging app and a production app to our pipeline. Both of these apps will be based on the same GitHub repo. To add a staging app, click the “Add app” button in the “Staging” section. Next, click “Create new app” to open a side panel. Create a new staging app In the side panel, give your app a name, choose an owner (yourself), and choose a region (I left mine in the United States). Then click “Create app” to confirm your changes. Configure your staging app Congrats, you’ve just created a staging app! Newly created staging app Now let’s do the same thing, but this time for our production app. When you’re done configuring your production app, you should see both apps in your pipeline: Heroku pipeline with a staging app and a production app Configuring Automatic Deploys We want our app to be deployed to our staging environment any time we commit to our repo’s main branch. To do this, click the dropdown button for the staging app and choose “Configure automatic deploys” from the menu. Configure automatic deploys In the dialog that appears, make sure the main branch is targeted, and check the box to “Wait for CI to pass before deploy.” In our next step, we’ll configure Heroku CI so that we can run tests in a CI pipeline. We don’t want to deploy our app to our staging environment unless CI is passing. Deploy the main branch to the staging app after CI passes Enabling Heroku CI If we’re going to require CI to pass, we better have something configured for CI! Navigate to the “Tests” tab and then click the “Enable Heroku CI” button. Enable Heroku CI Our demo app is built with Node and runs unit tests with Jest. The tests are run through the npm test script. Heroku CI allows you to configure more complicated CI setups using an app.json file, but in our case, because the test setup is fairly basic, Heroku CI can figure out which command to run without any additional configuration on our part. Pretty neat! Enabling Review Apps For the last part of our pipeline setup, let’s enable review apps. Review apps are temporary apps that get deployed for every pull request (PR) created in GitHub. They’re incredibly helpful when you want your code reviewer to review your changes manually. With a review app in place, the reviewer can simply open the review app rather than having to pull down the code onto their machine and run the app locally. To enable review apps, click the “Enable Review Apps” button on the pipeline page. Enable Review Apps In the dialog that appears, check all three boxes. The first box enables the automatic creation of review apps for each PR. The second box ensures that CI must pass before the review app can be created. The third box sets a time limit on how long a stale review app should exist until it is destroyed. Review apps use Heroku resources just like your regular apps, so you don’t want these temporary apps sitting around unused and costing you or your company more money. When you’re done with your configuration, click “Enable Review Apps” to finalize your changes. Configure your review apps Seeing It All in Action Alright, you made it! Let’s review what we’ve done so far. We created a Heroku pipeline. We created a staging app and a production app for that pipeline. We enabled automatic deploys for our staging app. We enabled Heroku CI to run tests for every PR. We enabled Heroku review apps to be created for every PR. Now let’s see it all in action. Create a PR in GitHub with any code change you’d like. I made a very minor UI change, updating the heading text from “Heroku Flow Demo” to “Heroku Flow Rules!” Right after the PR is created, you’ll note that a new “check” gets created in GitHub for the Heroku CI pipeline. GitHub PR check for the Heroku CI pipeline You can view the test output back in Heroku on your “Tests” tab: CI pipeline test output After the CI pipeline passes, you’ll note another piece of info gets appended to your PR in GitHub. The review app gets deployed, and GitHub shows a link to the review app. Click the “View deployment” button, and you’ll see a temporary Heroku app with your code changes in it. View deployment to see the review app You can also find a link to the review app in your Heroku pipeline: Review app found in the Heroku pipeline Let’s assume that you’ve gotten a code review and that everything looks good. It’s time to merge your PR. After you’ve merged your PR, look back at the Heroku pipeline. You’ll see that the staging app was automatically deployed since the new code was committed to the main branch. Staging app was automatically deployed At this point in the software development lifecycle, there might be some final QA or acceptance testing of the app in the staging environment. Let’s assume that everything still looks good and that you’re ready to release this change to your users. Click the “Promote to production” button on the staging app. This will open a dialog for you to confirm your action. Click “Promote” to confirm your changes. Promote to production After promoting the code, you’ll see the production app being deployed. Production app was deployed And with that, your changes are now in production for all of your users to enjoy. Nice work! Updated demo app with new changes in production Conclusion What a journey we’ve been through! In this short time together, we’ve configured everything we need for an enterprise-ready CI/CD solution. If you’d like to use a different CI/CD tool like GitLab CI/CD, GitHub Actions — or whatever else you may prefer — Heroku supports that as well. But if you don’t want to reach for a third-party CI/CD provider, now you can use Heroku with Heroku Flow.

By Tyler Hawkins

CORE

Three Reasons Why You Should Attend PlatformCon 2024

DZone is proud to announce our media partnership with PlatformCon 2024, one of the world’s largest platform engineering events. PlatformCon runs from June 10-14, 2024, and is primarily a virtual event, but there will also be a large live event in London, as well as some satellite events in other major cities. This event brings together a vibrant community of the most influential practitioners in the platform engineering and DevOps space to discuss methodologies, recommendations, challenges, and everything in between to help you build the perfect platform. Need help convincing your manager (or yourself) that this is an indispensable conference to attend? You’ve come to the right place! Below are three key reasons why you should attend PlatformCon24. 1. Platform Engineering Is a Hot Topic in 2024 So, what is platform engineering? In his most recent article on DZone, Mirco Hering describes a platform engineer as someone who plays three roles: the technical architect, the community enabler, and the product manager. This multifaceted approach helps to better streamline development practices and take the load off of software engineers and allow for each team to be more in sync with their deployment cycles. In 2024, we’ve seen an increase in articles and conversations on DZone around platform engineering, how it relates to DevOps, and the top considerations when looking to better optimize your development processes. Developers want to know more about this, and this conference is a perfect place to learn from the experts, and connect with other like minded individuals in the space. 2. Learn From Platform Engineering and DevOps Experts Have you seen the lineup of speakers for PlatformCon this year?! Industry leaders will help you navigate this space and key conference themes, with prominent names including Kelsey Hightower, Gregor Hohpe, Charity Majors Manuel Pais, Nicki Watt, Brian Finster, Mallory Haigh, and more. At DZone, we value peer-to-peer knowledge sharing, and find that the best way for developers to learn about new tech initiatives, methodologies, and approaches to existing practices is through the experiences of their peers. And this is exactly what PlatformCon is all about! This conference also gives attendees unparalleled access to the speakers via Slack channels. What better way to navigate the evolving world of platform engineering than to learn from the experts who are leading the way? 3. Embark on a Custom DevOps + Platform Engineering Journey As we mentioned earlier, platform engineering is multifaceted, and with that, the approaches and practices are as well. The five conference tracks highlighted below are intended to allow you to tailor your experience and platform engineering journey. Stories: This track enables you to learn from the practitioners who are building platforms at their organizations and will provide you with adoption tips of your own. Culture: This track focuses on the relationships between all of the developers and teams involved in platform engineering — from DevOps and site reliability engineers to software architects and more. Toolbox: This track focuses on the technical components of developer platforms, and dives into what tools and technologies developers use to solve for specific problems. Conversations will focus around IaC, GitOps, Kubernetes, and more. Impact: This track is all about the business side of platform engineering. It will dive into the key metrics that C-suite executives measure and will offer advice on how to get leadership buy-in to build a developer platform. Blueprint: This track will give you the foundation to build your own developer platform, covering important reference architectures and key design considerations. Register Today to Perfect Your Platform Now that we’ve shared multiple reasons why you should attend PlatformCon 2024, we’ll leave you with one final motivation — it’s free to register and attend! This conference is the perfect opportunity to connect with like-minded people in the developer space, learn more about platform engineering, and help determine the best next steps in your developer platform journey. Learn more about how to register here. See you there!

By Caitlin Candelmo

DevSecOps: It’s Time To Pay for Your Demand, Not Ingestion

I remember back when mobile devices started to gain momentum and popularity. While I was excited about a way to stay in touch with friends and family, I was far less excited about limits being placed on call length minutes and the number of text messages I could utilize … before being forced to pay more. Believe it or not, the #646 (#MIN) and #674 (#MSG) contact entries were still lingering in my address book until a recent clean-up effort. At one time, those numbers provided a handy mechanism to determine how close I was to hitting the monthly limits enforced by my service provider. Along some very similar lines, I recently found myself in an interesting position as a software engineer – figuring out how to log less to avoid exceeding log ingestion limits set by our observability platform provider. I began to wonder how much longer this paradigm was going to last. The Toil of Evaluating Logs for Ingestion I remember the first time my project team was contacted because log ingestion thresholds were exceeding the expected limit with our observability partner. A collection of new RESTful services had recently been deployed in order to replace an aging monolith. From a supportability perspective, our team had made a conscious effort to provide the production support team with a great deal of logging – in the event the services did not perform as expected. There were more edge cases than there were regression test coverage, so we were expecting alternative flows to trigger results that would require additional debugging if they did not process as expected. Like most cases, the project had aggressive deadlines that could not be missed. When we were instructed to “log less” an unplanned effort became our priority. The problem was, we weren’t 100% certain how best to proceed. We didn’t know what components were in a better state of validation (to have their logs reduced), and we weren’t exactly sure how much logging we would need to remove to no longer exceed the threshold. To our team, this effort was a great example of what has become known as toil: “Toil is the kind of work that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and that scales linearly as a service grows.” – Eric Harvieux (Google Site Reliability Engineering) Every minute our team spent on reducing the amount of logs ingested into the observability platform came at the expense of delivering fewer features and functionality for our services. After all, this was our first of many planned releases. Seeking a “Log Whatever You Feel Necessary” Approach What our team really needed was a scenario where our observability partner was fully invested in the success of our project. In this case, it would translate to a “log whatever you feel necessary” approach. Those who have walked this path before will likely be thinking “this is where JV has finally lost his mind.” Stay with me here as I think I am on to something big. Unfortunately, the current expectation is that the observability platform can place limits on the amount of logs that can be ingested. The sad part of this approach is that, in doing so, observability platforms put their needs ahead of their customers – who are relying on and paying for their services. This is really no different from a time when I relied on the #MIN and #MSG contacts in my phone to make sure I lived within the limits placed on me by my mobile service provider. Eventually, my mobile carrier removed those limits, allowing me to use their services in a manner that made me successful. The bottom line here is that consumers leveraging observability platforms should be able to ingest whatever they feel is important to support their customers, products, and services. It’s up to the observability platforms to accommodate the associated challenges as customers desire to ingest more. This is just like how we engineer our services in a demand-driven world. I cannot imagine telling my customer, “Sorry, but you’ve given us too much to process this month.” Pay for Your Demand – Not Ingestion The better approach here is the concept of paying for insights and not limiting the actual log ingestion. After all, this is 2024 – a time when we all should be used to handling massive quantities of data. The “pay for your demand – not ingestion” concept has been considered a “miss” in the observability industry… until recently when I read that Sumo Logic has disrupted the DevSecOps world by removing limits on log ingestion. This market-disruptor approach embraces the concept of “log whatever you feel necessary” with a north star focused on eliminating silos of log data that were either disabled or skipped due to ingestion thresholds. Once ingested, AI/ML algorithms help identify and diagnose issues – even before they surface as incidents and service interruptions. Sumo Logic is taking on the burden of supporting additional data because they realize that customers are willing to pay a fair price for the insights gained from their approach. So what does this new strategy to observability cost expectations look like? It can be difficult to pinpoint exactly, but as an example, if your small-to-medium organization is producing an average of 25 MB of log data for ingestion per hour, this could translate into an immediate 10-20% savings (using Sumo Logic’s price estimator) on your observability bill. In taking this approach, every single log is available in a custom-built platform that scales along with an entity’s observability growth. As a result, AI/ML features can draw upon this information instantly to help diagnose problems – even before they surface with consumers. When I think about the project I mentioned above, I truly believe both my team and the production support team would have been able to detect anomalies faster than what we were forced to implement. Instead, we had to react to unexpected incidents that impacted the customer’s experience. Conclusion I was able to delete the #MIN and #MSG entries from my address book because my mobile provider eliminated those limits, providing a better experience for me, their customer. My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” – J. Vester In 2023, I also started thinking hard about toil and making a conscious effort to look for ways to avoid or eliminate this annoying productivity killer. The concept of “zero dollar ingest” has disrupted the observability market by taking a lead from the mobile service provider's playbook. Eliminating log ingestion thresholds puts customers in a better position to be successful with their own customers, products, and services (learn more about Sumo Logic’s project here). From my perspective, not only does this adhere to my mission statement, it provides a toil-free solution to the problem of log ingestion, data volume, and scale. Have a really great day!

By John Vester

CORE

Cybersecurity in the Cloud: Integrating Continuous Security Testing Within DevSecOps

Cloud computing has revolutionized software organizations' operations, offering unprecedented scalability, flexibility, and cost-efficiency in managing digital resources. This transformative technology enables businesses to rapidly deploy and scale services, adapt to changing market demands, and reduce operational costs. However, the transition to cloud infrastructure is challenging. The inherently dynamic nature of cloud environments and the escalating sophistication of cyber threats have made traditional security measures insufficient. In this rapidly evolving landscape, proactive and preventative strategies have become paramount to safeguard sensitive data and maintain operational integrity. Against this backdrop, integrating security practices within the development and operational workflows—DevSecOps—has emerged as a critical approach to fortifying cloud environments. At the heart of this paradigm shift is Continuous Security Testing (CST), a practice designed to embed security seamlessly into the fabric of cloud computing. CST facilitates the early detection and remediation of vulnerabilities and ensures that security considerations keep pace with rapid deployment cycles, thus enabling a more resilient and agile response to potential threats. By weaving security into every phase of the development process, from initial design to deployment and maintenance, CST embodies the proactive stance necessary in today's cyber landscape. This approach minimizes the attack surface and aligns with cloud services' dynamic and on-demand nature, ensuring that security evolves in lockstep with technological advancements and emerging threats. As organizations navigate the complexities of cloud adoption, embracing Continuous Security Testing within a DevSecOps framework offers a comprehensive and adaptive strategy to confront the multifaceted cyber challenges of the digital age. Most respondents (96%) of a recent software security survey believe their company would benefit from DevSecOps' central idea of automating security and compliance activities. This article describes the details of how CST can strengthen your cloud security and how you can integrate it into your cloud architecture. Key Concepts of Continuous Security Testing Continuous Security Testing (CST) helps identify and address security vulnerabilities in your application development lifecycle. Using automation tools, it analyzes your complete security structure and discovers and resolves the vulnerabilities. The following are the fundamental principles behind it: Shift-left approach: CST promotes early adoption of safety measures by bringing security testing and mitigation to the start of the software development lifecycle. This method reduces the possibility of vulnerabilities in later phases by assisting in the early detection and resolution of security issues. Automated security testing: Critical to CST is automation, which allows for consistent and quick evaluation of security measures, scanning for vulnerabilities, and code analysis. Automation ensures consistent and rapid security evaluation. Continuous monitoring and feedback: As part of CST, safety incidents and feedback chains are monitored in real-time, allowing security vulnerabilities to be identified and fixed quickly. Integrating Continuous Security Testing Into the Cloud Let's explore the phases involved in integrating CST into cloud environments. Laying the Foundation for Continuous Security Testing in the Cloud To successfully integrate Continuous Security Testing (CST), you must prepare your cloud environment first. Use a manual tool like OWASP or an automated security testing process to perform a thorough security audit and ensure your cloud environments are well-protected to lay a robust groundwork for CST. Before diving into integrating Continuous Security Testing (CST) within your cloud infrastructure, it's crucial to lay a solid foundation by meticulously preparing your cloud environment. This preparatory step involves conducting a comprehensive security audit to identify vulnerabilities and ensure your cloud architecture is fortified against threats. Leveraging tools such as the Open Web Application Security Project (OWASP) for manual evaluations or employing sophisticated automated security testing processes can significantly aid this endeavor. Conduct a detailed inventory of all assets and resources within your cloud architecture to assess your cloud environment's security posture. This includes everything from data storage solutions and archives to virtual machines and network configurations. By understanding the full scope of your cloud environment, you can better identify potential vulnerabilities and areas of risk. Next, systematically evaluate these components for security weaknesses, ensuring no stone is left unturned. This evaluation should encompass your cloud infrastructure's internal and external aspects, scrutinizing access controls, data encryption methods, and the security protocols of interconnected services and applications. Identifying and addressing these vulnerabilities at this stage sets a robust groundwork for the seamless integration of Continuous Security Testing, enhancing your cloud environment's resilience to cyber threats and ensuring a secure, uninterrupted operation of cloud-based services. By undertaking these critical preparatory steps, you position your organization to leverage CST effectively as a dynamic, ongoing practice that detects emerging threats in real-time and integrates security seamlessly into every phase of your cloud computing operations. Establishing Effective Security Testing Criteria The cornerstone of implementing Continuous Security Testing (CST) within cloud ecosystems is meticulously defining the security testing requirements. This pivotal step involves identifying a holistic suite of testing methodologies encompassing your security landscape, ensuring thorough coverage and protection against potential vulnerabilities. A multifaceted approach to security testing is essential for a robust defense strategy. This encompasses a variety of criteria, such as: Vulnerability scanning: Systematic examination of your cloud environment to identify and classify security loopholes. Penetration testing: Simulated cyber attacks against your system to evaluate the effectiveness of security measures. Compliance inspections: Assessments to ensure that cloud operations adhere to industry standards and regulatory requirements. Source code analysis: Examination of application source code to detect security flaws or vulnerabilities. Configuration analysis: Evaluation of system configurations to identify security weaknesses stemming from misconfigurations or outdated settings. Container security analysis: Analysis focused on the security of containerized applications, including their deployment, management, and orchestration. Organizations can proactively identify and rectify security vulnerabilities within their cloud architecture by selecting the appropriate mix of these testing criteria. This proactive stance enhances the overall security posture and embeds a culture of continuous improvement and vigilance across the cloud computing landscape. Adopting a comprehensive and systematic approach to security testing ensures that your cloud environment remains resilient against evolving cyber threats, safeguarding your critical assets and data effectively. Choosing the Right Security Testing Tools for Automation The transition to automated security testing tools is critical for achieving faster and more accurate security assessments, significantly reducing the manual effort, workforce involvement, and resources dedicated to routine tasks. A diverse range of tools exists to support this need, including Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and safety measures for Infrastructure as Code (IaC) etc. These technologies are easy to integrate into Continuous Integration/Continuous Deployment (CI/CD) pipelines and improve security by finding and fixing vulnerabilities before development. More than half of DevOps teams conduct SAST scans, 44% conduct DAST scans, and almost 50% inspect containers and dependencies as part of their security measures. However, when choosing the right tool for automation, consider features like ease of use, the ability to get updated with the vulnerability, and ROI vs. the cost of the tool. When choosing the right automation tools, evaluating them based on several critical factors beyond their primary functionalities is vital. The ease of integration into existing workflows, their capacity for timely updates in response to new vulnerabilities, and the balance between their cost and the return on investment they offer are crucial considerations. These factors ensure that the selected tools enhance security measures and align with the organization's overall security strategy and resource allocation, facilitating a more secure and efficient development lifecycle. Continuous Monitoring and Improvement The bedrock of maintaining an up-to-date and secure cloud infrastructure lies in the practices of continuous monitoring and iterative improvement throughout the entirety of its lifecycle. Integrate your cloud log with Security Information and Event Management (SIEM) capabilities to get centralized security intelligence and initiate continuous monitoring and improvement. Similarly, ELK Stack (Elasticsearch, Logstash, Kibana) is another tool that can help you visualize, collect, and analyze your log data. Regularly monitoring your security landscape and adapting based on the insights gleaned from testing and monitoring outputs are essential. Such a proactive approach not only aids in preemptively identifying and mitigating potential threats but also ensures that your security framework remains robust and adaptive to the ever-evolving cyber threat landscape. Strategic Risk Management and Mitigation Efforts Effective security management requires a strategic approach to evaluating and mitigating vulnerabilities, guided by their criticality, exploitability, and potential repercussions for the organization. Utilizing threat modeling techniques enables a targeted allocation of resources, focusing on areas of highest risk to reduce exposure and avert potential security incidents. Following identifying critical vulnerabilities, devising and executing a comprehensive risk mitigation strategy is imperative. This strategy should encompass a range of solutions tailored to diminish the identified risks, including the deployment of software patches and updates, the establishment of enhanced security protocols, the integration of additional safeguarding measures, or even the strategic overhaul of existing systems and processes. Organizations can fortify their defenses by prioritizing and systematically addressing vulnerabilities based on severity and impact, ensuring a more secure and resilient operational environment. Benefits of Continuous Security Testing in the Cloud There are numerous benefits of using continuous security testing in cloud environments. Early vulnerability detection: Using CST, you can identify security issues early on and address them before they pose a risk. Enhanced security quality: To better defend your cloud infrastructure against cyberattacks, security testing gives it an additional layer of protection. Enhanced innovation and agility: CST enables faster release cycles by identifying risks early on, allowing you to take proactive measures to counter them. Enhanced team collaboration: CST promotes collaboration between different teams to cultivate a culture of collective accountability for security. Compliance with industry standards: By routinely assessing its security controls and procedures, you can lessen the possibility of fines and penalties for noncompliance with corporate policies and legal requirements. Conclusion In the rapidly evolving landscape of cloud computing, Continuous Security Testing (CST) emerges as a cornerstone for safeguarding cloud environments against pervasive cyber threats. By weaving security seamlessly into the development fabric through automation and vigilant monitoring, CST empowers organizations to detect and neutralize vulnerabilities preemptively. The adoption of CST transcends mere risk management; it fosters an environment where security, innovation, and collaboration converge, propelling businesses forward. This synergistic approach elevates organizations' security posture and instills a culture of continuous improvement and adaptability. As businesses navigate the complexities of the digital age, implementing CST positions them to confidently address the dynamic nature of cyber threats, ensuring resilience and securing their future in the cloud.

By Prithvish Kovelamudi

Revolutionizing Software Deployment: The Synergy of Cloud and DevOps

In the contemporary digital landscape, the amalgamation of cloud computing and DevOps methodologies stands as a beacon of innovation, reshaping the contours of software delivery. This confluence paves the way for a seamless, agile, and robust development process, fundamentally altering the traditional paradigms of software engineering. By exploring the depths of this integration, we can unveil the transformative potential it holds for businesses striving for efficiency and competitiveness. Unveiling the Fusion of Cloud and DevOps At the heart of this integration lies a mutual objective: to streamline the development and deployment processes, thereby enhancing productivity and operational flexibility. Cloud computing dismantles the conventional constraints of hardware infrastructure, offering scalable resources on an on-demand basis. Parallelly, DevOps cultivates a culture that bridges the gap between development and operations teams, emphasizing continuous improvement, automation, and swift feedback cycles. The synthesis of Cloud and DevOps injects dynamism into the development lifecycle, enabling a symbiotic relationship where infrastructure evolves in concert with the applications it hosts. Such an environment is ripe for adopting practices like Infrastructure as Code (IaC) and Continuous Integration/Continuous Deployment (CI/CD), which automate and accelerate deployment tasks, significantly reducing manual intervention and the margin for error. Extending Infrastructure Automation: A Comprehensive Example To further elucidate the practical implications of Cloud and DevOps synergy, consider an expanded scenario involving the deployment of a scalable and secure web application architecture in the cloud. This intricate Python script showcases the use of AWS CloudFormation to automate the deployment of a web application, complete with a front-end, a back-end database, a load balancer for traffic management, and an auto-scaling setup for dynamic resource allocation: Python import boto3 # Define a detailed CloudFormation template for a scalable web application architecture template = """ Resources: AutoScalingGroup: Type: 'AWS::AutoScaling::AutoScalingGroup' Properties: AvailabilityZones: ['us-east-1a'] LaunchConfigurationName: Ref: LaunchConfig MinSize: '1' MaxSize: '3' TargetGroupARNs: - Ref: TargetGroup LaunchConfig: Type: 'AWS::AutoScaling::LaunchConfiguration' Properties: ImageId: 'ami-0c55b159cbfafe1f0' InstanceType: 't2.micro' TargetGroup: Type: 'AWS::ElasticLoadBalancingV2::TargetGroup' Properties: Port: 80 Protocol: HTTP VpcId: 'vpc-123456' LoadBalancer: Type: 'AWS::ElasticLoadBalancingV2::LoadBalancer' Properties: Subnets: - 'subnet-123456' DatabaseServer: Type: 'AWS::RDS::DBInstance' Properties: DBInstanceClass: 'db.t2.micro' Engine: 'MySQL' MasterUsername: 'admin' MasterUserPassword: 'your_secure_password' AllocatedStorage: '20' """ # Initialize CloudFormation client cf = boto3.client('cloudformation') # Deploy the stack response = cf.create_stack( StackName='ScalableWebAppStack', TemplateBody=template, Parameters=[], TimeoutInMinutes=20, Capabilities=['CAPABILITY_IAM'] ) print("Stack creation initiated:", response) This script embodies the complexity and sophistication that Cloud and DevOps integration brings to infrastructure deployment. By orchestrating a multi-tier architecture complete with auto-scaling and load balancing, it illustrates how automated processes can significantly enhance application resilience, scalability, and performance. Expanding the Benefits The amalgamation of Cloud and DevOps extends beyond mere technical advantages, permeating various aspects of organizational culture and operational philosophy: Strategic Innovation This integration facilitates a strategic approach to innovation, allowing teams to experiment and iterate rapidly without the fear of failure or excessive costs, thus fostering a culture of continuous improvement. Market Responsiveness Businesses gain the agility to respond swiftly to market changes and customer demands, ensuring that they can adapt strategies and products in real time to maintain competitiveness. Security and Compliance Automated deployment models incorporate security best practices and compliance standards from the outset, embedding them into the fabric of the development process and minimizing vulnerabilities. Environmental Sustainability Cloud providers invest heavily in energy-efficient data centers, enabling organizations to reduce their carbon footprint by leveraging cloud infrastructure, contributing to more sustainable operational practices. Workforce Empowerment The collaborative nature of DevOps, combined with the flexibility of the Cloud, empowers teams by providing them with the tools and autonomy to innovate, make decisions, and take ownership of their work, leading to higher satisfaction and productivity. Navigating Towards a Digital Future The fusion of cloud computing and DevOps is not merely a trend but a fundamental shift in the digital paradigm, catalyzing the transformation of software delivery into a more agile, efficient, and responsive process. This synergy not only accelerates the pace of innovation but also enhances the ability of businesses to adapt to the ever-changing digital landscape, ensuring they remain at the forefront of their respective industries. As organizations navigate toward this digital future, the integration of Cloud and DevOps stands as a pivotal strategy. It enables the creation of resilient, scalable, and innovative software solutions that can meet the demands of the modern consumer and adapt to the challenges of the digital era. The comprehensive example provided illustrates the practical application of these principles, showcasing how businesses can leverage automation to streamline their development processes, reduce costs, and enhance service reliability. The journey towards embracing Cloud and DevOps requires a cultural shift within organizations, one that promotes collaboration, continuous learning, and a willingness to embrace new technologies. By fostering an environment that values innovation and agility, businesses can unlock the full potential of their teams and technologies, driving growth and sustaining competitiveness in an increasingly digital world. In conclusion, the convergence of Cloud and DevOps is more than just a technological evolution; it is a strategic imperative for any organization looking to thrive in the digital age. By adopting this integrated approach, businesses can enhance their software delivery processes, foster innovation, and achieve operational excellence. The future belongs to those who can harness the power of Cloud and DevOps to transform their ideas into reality, rapidly and efficiently.

By Bhargavi Gorantla

Leveraging Feature Flags With IBM Cloud App Configuration in React Applications

In modern application development, delivering personalized and controlled user experiences is paramount. This necessitates the ability to toggle features dynamically, enabling developers to adapt their applications in response to changing user needs and preferences. Feature flags, also known as feature toggles, have emerged as a critical tool in achieving this flexibility. These flags empower developers to activate or deactivate specific functionalities based on various criteria such as user access, geographic location, or user behavior. React, a popular JavaScript framework known for its component-based architecture, is widely adopted in building user interfaces. Given its modular nature, React applications are particularly well-suited for integrating feature flags seamlessly. In this guide, we'll explore how to integrate feature flags into your React applications using IBM App Configuration, a robust platform designed to manage application features and configurations. By leveraging feature flags and IBM App Configuration, developers can unlock enhanced flexibility and control in their development process, ultimately delivering tailored user experiences with ease. IBM App Configuration can be integrated with any framework be it React, Angular, Java, Go, etc. React is a popular JavaScript framework that uses a component-based architecture, allowing developers to build reusable and modular UI components. This makes it easier to manage complex user interfaces by breaking them down into smaller, self-contained units. Adding feature flags to React components will make it easier for us to handle the components. Integrating With IBM App Configuration IBM App Configuration provides a comprehensive platform for managing feature flags, environments, collections, segments, and more. Before delving into the tutorial, it's important to understand why integrating your React application with IBM App Configuration is necessary and what benefits it offers. By integrating with IBM App Configuration, developers gain the ability to dynamically toggle features on and off within their applications. This capability is crucial for modern application development, as it allows developers to deliver controlled and personalized user experiences. With feature flags, developers can activate or deactivate specific functionalities based on factors such as user access, geographic location, or user preferences. This not only enhances user experiences but also provides developers with greater flexibility and control over feature deployments. Additionally, IBM App Configuration offers segments for targeted rollouts, enabling developers to gradually release features to specific groups of users. Overall, integrating with IBM App Configuration empowers developers to adapt their applications' behavior in real time, improving agility, and enhancing user satisfaction. To begin integrating your React application with App Configuration, follow these steps: 1. Create an Instance Start by creating an instance of IBM App Configuration on cloud.ibm.com. Within the instance, create an environment, such as Dev, to manage your configurations. Now create a collection. Creating collections comes in handy when there are multiple feature flags created for various projects. Each project can have a collection in the same App Configuration instance and you can tag these feature flags to the collection to which they belong. 2. Generate Credentials Access the service credentials section and generate new credentials. These credentials will be required to authenticate your React application with App Configuration. 3. Install SDK In your React application, install the IBM App Configuration React SDK using npm: CSS npm i ibm-appconfiguration-react-client-sdk 4. Configure Provider In your index.js or App.js, wrap your application component with AppConfigProvider to enable AppConfig within your React app. The Provider must be wrapped at the main level of the application, to ensure the entire application has access. The AppConfigProvider requires various parameters as shown in the screenshot below. All of these values can be found in the credentials created. 5. Access Feature Flags Now, within your App Configuration instance, create feature flags to control specific functionalities. Copy the feature flag ID for further integration into your code. Integrating Feature Flags Into React Components Once you've set up the AppConfig in your React application, you can seamlessly integrate feature flags into your components. Enable Components Dynamically Use the feature flag ID copied from the App Configuration instance to toggle specific components based on the flag's status. This allows you to enable or disable features dynamically without redeploying your application. Utilizing Segments for Targeted Rollouts IBM App Configuration offers segments to target specific groups of users, enabling personalized experiences and controlled rollouts. Here's how to leverage segments effectively: Define Segments Create segments based on user properties, behaviors, or other criteria to target specific user groups. Rollout Percentage Adjust the rollout percentage to control the percentage of users who receive the feature within a targeted segment. This enables gradual rollouts or A/B testing scenarios. Example If the rollout percentage is set to 100% and a particular segment is targeted, then the feature is rolled out to all the users in that particular segment. If the rollout percentage is set between 1% to 99% and the rollout percentage is 60%, for example, and a particular segment is targeted, then the feature is rolled out to randomly 60% of the users in that particular segment. If the rollout percentage is set to 0% and a particular segment is targeted, then the feature is rolled out to none of the users in that particular segment. Conclusion Integrating feature flags with IBM App Configuration empowers React developers to implement dynamic feature toggling and targeted rollouts seamlessly. By leveraging feature flags and segments, developers can deliver personalized user experiences while maintaining control over feature deployments. Start integrating feature flags into your React applications today to unlock enhanced flexibility and control in your development process.

By Pradeep Gopalgowda

Software Engineering Trends in the Industry

This article identifies some basic trends in the software industry. Specifically, we will explore how some well-known organizations implement and benefit from early and continuous testing, faster software delivery, reduced costs, and increased collaboration. While it is clear that activities like breaking down silos, shift-left testing, automation, and continuous delivery are interrelated, it is beneficial to take a look at how companies strive to achieve such goals in practice. Companies try to break down the traditional silos that separate development, operations, and testing teams. This eliminates barriers and builds collaboration, where all teams share responsibility for quality throughout the software development lifecycle. This collaborative approach leads to improved problem-solving, faster issue resolution, and ultimately, higher-quality software. The concept of "shifting left" emphasizes integrating testing activities earlier into the development process. This means conducting tests as code is written (unit tests) and throughout development stages (integration tests), instead of waiting until the end. By detecting and fixing defects earlier, the overall development cycle becomes more efficient as issues are addressed before they become complex and expensive to fix later. This proactive approach ultimately leads to higher-quality software and faster releases. Embracing automation is another core trend. By utilizing automated testing tools and techniques, such as unit testing frameworks and continuous integration pipelines, organizations can significantly accelerate the testing process. This frees up valuable human resources, allowing testers to focus on more complex tasks like exploratory testing, test strategy development, and collaborating with other teams. This increases efficiency, it allows faster feedback loops and earlier identification of defects, ultimately leading to higher-quality software and faster releases. Continuous delivery, ensuring high-quality software is delivered frequently and reliably is another key trend. This is achieved through several key practices: automation of repetitive tasks, integration and testing throughout development, and streamlined deployment pipelines. By catching and addressing issues early, fewer defects reach production, enabling faster and more reliable releases of high-quality software that meets user expectations. This continuous cycle of delivery and improvement ultimately leads to increased innovation and a competitive edge. Early and Continuous Testing Early and continuous testing may lead to better defect detection and faster resolution, resulting in higher-quality software. Let's take a look at a few specific cases: 1. Netflix Challenge Netflix's challenge is releasing new features regularly while maintaining a high level of quality across various devices and platforms. Solution Netflix adopted a DevOps approach with extensive automation testing. They utilize unit tests that run on every code commit, catching bugs early. Additionally, they have automated testing frameworks for various functionalities like UI, API, and performance. Impact This approach allows them to identify and fix issues quickly, preventing them from reaching production and impacting user experience. 2. Amazon Challenge Amazon's challenge is ensuring the reliability and scalability of their massive e-commerce platform to handle unpredictable traffic spikes. Solution Amazon employs a "chaos engineering" practice. They intentionally introduce controlled disruptions into their systems through automated tools, simulating real-world scenarios like server failures or network outages. This proactive testing helps them uncover potential vulnerabilities and weaknesses before they cause customer disruptions. Impact By identifying and addressing potential issues proactively, Amazon can ensure their platform remains highly available and reliable, providing a seamless experience for millions of users. 3. Spotify Challenge Spotify's challenge is maintaining a seamless music streaming experience across various devices and network conditions. Solution Spotify heavily utilizes continuous integration and continuous delivery (CI/CD) pipelines, integrating automated tests at every stage of the development process. This includes unit tests, integration tests, and performance tests. Impact Early detection and resolution of issues through automation allow them to maintain a high level of quality and deliver frequent app updates with new features and bug fixes. This results in a more stable and enjoyable user experience for music lovers globally. These examples highlight how various organizations across different industries leverage early and continuous testing to: Catch defects early: Automated tests identify issues early in the development cycle, preventing them from cascading into later stages and becoming more complex and expensive to fix. Resolve issues faster: Early detection allows for quicker bug fixes, minimizing potential disruptions and ensuring a smoother development process. Deliver high-quality software: By addressing issues early and continuously, organizations can deliver software that meets user expectations and performs reliably. By embracing early and continuous testing, companies can achieve a faster time-to-market, reduced development costs, and ultimately, a more satisfied customer base. Faster Software Delivery Emphasizing automation and continuous integration empowers organizations to achieve faster software delivery. Here are some examples showcasing how: 1. Netflix Challenge Netflix's challenge is maintaining rapid release cycles for new features and bug fixes while ensuring quality. Solution Netflix utilizes a highly automated testing suite encompassing unit tests, API tests, and UI tests. These tests run automatically on every code commit, providing immediate feedback on potential issues. Additionally, they employ a continuous integration and delivery (CI/CD) pipeline that automatically builds, tests, and deploys code to production environments. Impact Automation reduces the need for manual testing, significantly reducing testing time and allowing for faster feedback loops. The CI/CD pipeline further streamlines deployment, enabling frequent releases without compromising quality. This allows Netflix to deliver new features and bug fixes to users quickly, keeping them engaged and satisfied. 2. Amazon Challenge Amazon's challenge is scaling deployments and delivering new features to their massive user base quickly and efficiently. Solution Amazon heavily invests in infrastructure as code (IaC) tools. These tools allow them to automate infrastructure provisioning and configuration, ensuring consistency and repeatability across different environments. Additionally, they leverage a robust CI/CD pipeline that integrates automated testing with infrastructure provisioning and deployment. Impact IaC reduces manual configuration errors and streamlines infrastructure setup, saving significant time and resources. The integrated CI/CD pipeline allows for automated deployments, reducing the time required to move code from development to production. This enables Amazon to scale efficiently and deliver new features and services to their users at an accelerated pace. 3. Spotify Challenge Spotify's challenge is keeping up with user demand and delivering new features and updates frequently. Solution Spotify utilizes a containerized microservices architecture, breaking its application down into smaller, independent components. This allows for independent development, testing, and deployment of individual services. Additionally, they have invested heavily in automated testing frameworks and utilize a continuous integration and delivery pipeline. Impact The microservices architecture enables individual teams to work on and deploy features independently, leading to faster development cycles. Automated testing provides rapid feedback, allowing for quick identification and resolution of issues. The CI/CD pipeline further streamlines deployment, allowing for frequent releases of new features and updates to the Spotify platform and keeping users engaged with fresh content and functionalities. These examples demonstrate how companies across various sectors leverage automation and continuous integration to achieve: Reduced testing time: Automated testing reduces the need for manual efforts, significantly reducing the time it takes to test and identify issues. Faster feedback loops: Automated tests provide immediate feedback on code changes, allowing developers to address issues quickly and iterate faster. Streamlined deployment: Continuous integration and delivery pipelines automate deployments, minimizing manual intervention and reducing the time it takes to move code to production. By leveraging automation and continuous integration, organizations can enjoy faster time-to-market, increased responsiveness to user needs, and a competitive edge in their respective industries. Reduced Costs Automating repetitive tasks and shifting left can reduce the overall cost of testing. There are three main areas to highlight here. 1. Reduced Manual Effort Imagine a company manually testing a new e-commerce website across different browsers and devices. This would require a team of testers and significant time, leading to high labor costs. By automating these tests, the company can significantly reduce the need for manual testing, freeing up resources for more complex tasks and strategic testing initiatives. 2. Early Defect Detection and Resolution A software company traditionally performed testing only towards the end of the development cycle. This meant that bugs discovered late in the process were more expensive to fix due to a number of reasons. By shifting left and implementing automated unit tests early on, the company can identify and fix bugs early in the development cycle, minimizing the cost of rework and reducing the chance of them cascading into later stages. 3. Improved Test Execution Speed A software development team manually ran regression tests after every code change, causing lengthy delays and hindering development progress. By automating these tests, the team can run them multiple times a day, providing faster feedback and enabling developers to iterate more quickly. This reduces overall development time and associated costs. Examples Capgemini: Implemented automation for 70% of their testing efforts, resulting in a 50% reduction in testing time and a 20% decrease in overall project costs. Infosys: Embraced automation testing, leading to a 40% reduction in manual effort and a 30% decrease in testing costs. Barclays Bank: Shifted left by introducing unit and integration testing, achieving a 25% reduction in defect escape rate and a 15% decline in overall testing costs. These examples showcase how companies across different sectors leverage automation and shifting left to achieve the following: Reduced labor costs: Automating repetitive testing tasks reduces the need for manual testers, leading to significant cost savings. Lower rework costs: Early defect detection and resolution minimize the need for rework later in the development cycle, saving time and money. Increased development efficiency: Faster test execution speeds through automation allow developers to iterate more quickly and reduce overall development time, leading to cost savings. By embracing automation and shifting left, organizations can enjoy improved resource utilization, reduced project overruns, and a better return on investment (ROI) for their software development efforts. Increased Collaboration Increased collaboration between development (Dev), operations (Ops), and testing teams. This is achieved by creating a shared responsibility for quality throughout the software development lifecycle. Here's how it works: Traditional Silos vs. Collaborative Approach Traditional Silos In a siloed environment, each team operates independently. Developers write code, testers find bugs, and operations manage the production environment. This often leads to finger-pointing, delays, and a disconnect between teams. Collaborative Approach DevOps, QAOps, and agile practices, among others, break down these silos and promote shared ownership for quality. Developers write unit tests, operations implement automated infrastructure testing, and testers focus on higher-level testing and test strategy. This nurtures collaboration, communication, and a shared sense of accountability. Examples Netflix: Utilizes a cross-functional team structure with members from development, operations, and testing working together. This allows them to share knowledge, identify and resolve issues collaboratively, and ensure a smooth delivery process. Amazon: Employs a "blameless post-mortem" culture where teams analyze incidents collaboratively without assigning blame. This builds openness, encourages shared learning, and ultimately improves system reliability. Spotify: Implements a "one team" approach where developers, operations engineers, and testers work together throughout the development cycle. This facilitates open communication, allows for shared decision-making, and promotes a sense of collective ownership for the product's success. Benefits of Increased Collaboration Improved problem-solving: By working together, teams can leverage diverse perspectives and expertise to identify and resolve issues more effectively. Faster issue resolution: Open communication allows for quicker sharing of information and faster identification of the root cause of problems. Enhanced quality: Increased collaboration creates a culture of ownership and accountability, leading to higher-quality software. Improved team morale: Collaborative work environments are often more enjoyable and motivating for team members, leading to increased productivity and job satisfaction. Strategies for Fostering Collaboration Cross-functional teams: Encourage collaboration by forming teams with members from different disciplines. Shared goals and metrics: Align teams around shared goals and success metrics that promote collective responsibility for quality. Open communication: Create open communication channels and encourage information sharing across teams. Knowledge sharing: Facilitate knowledge sharing across teams through workshops, training sessions, and collaborative problem-solving activities. By adopting DevOps, QAOps, and agile principles, organizations can break down silos, embrace shared responsibility, and cultivate a culture of collaboration. This leads to a more efficient, innovative, and, ultimately, successful software development process. Wrapping Up A number of organizations embark on a transformative journey towards faster, more reliable, and higher-quality software delivery. Through breaking down silos and forging shared responsibility, teams can leverage automation and shift left testing to enhance continuous delivery. This collaborative and efficient approach empowers organizations to deliver high-quality software more frequently, reduce costs, and ultimately gain a competitive edge in the ever-evolving technology landscape.

By Stelios Manioudakis, PhD

CORE

May	JUN	Jul
	16
2023	2024	2025

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