How Dagster outperforms Airflow for lakehouse orchestration

As year end approaches, many data teams are beginning to decide which projects will make their 2026 roadmaps. If improving on legacy orchestration is on your radar, check out this blog from Eric Thomas

Data doesn’t always come neatly packaged. Sometimes, we collect it manually, but often, it’s fetched from APIs that have their own rules and limits. Let's understand API rate limit in action which is a way servers protect themselves from overload. It reminded me how theory meets practice: managing data pipelines isn’t just about cleaning or transforming data, but also about respecting the flow of data. Here’s what I keep in mind now: ✅ Always check API docs for X-RateLimit-Limit and Retry-After headers ✅ Use time.sleep() or exponential backoff to avoid spamming servers ✅ Cache or store responses to minimize redundant requests Good data engineering is about being a good API citizen - balancing automation with responsibility. #API #DataAcquisition #DataPreprocessing #Python #DataEngineering #MastersJourney #TechLearning

At Foundations 2025 Andrew Chabot of FinThrive and Eric Tome of Databricks offered a real-world demo of combining a governed CData Software semantic layer with Databricks Genie to accelerate time-to-insight, enable smarter automation, and support rapid experimentation.    “Does Databricks understand what's behind? No. Databricks doesn't care. Databricks just knows assets, locations, and sensors right here. And what's great about some of this Data Shop functionality is if I go in here and you click to see Python, it'll pull up this Python right here and pretty much give you the code that you need to run in Databricks to make your connection and actually pull data out of the endpoint and hydrate your lakehouse.” -- Andrew Chabot, FinThrive      Access their session in FULL along with other insights from Foundations speakers: https://bit.ly/44K8MbW    #CData #CDataFoundations #Databricks #DatabricksGenie #Lakehouse #SemanticLayer #DataVirtualization

❓ What makes a data ingestion & transformation framework truly reusable and scalable? Beyond tools and pipelines, the magic lies in the common components that can be applied across projects 👇 🔹 Schema Validation & Enforcement - ensure data conforms to contracts (Avro, JSON, Protobuf). 🔹 Deduplication & Filtering - avoid reprocessing and keep your data clean. 🔹 Metadata & Audit Logging - track source, version, and ingestion timestamps for every record. 🔹 Retry & Error Handling - resilient ingestion with DLQs and exponential backoff. 🔹 Reusable Transform Functions - standardized dbt macros, Spark UDFs, or Python modules. 🔹 Lineage & Quality Tracking - measure data accuracy, completeness, and trace dependencies. Each of these turns raw data movement into a reliable, production-grade system that scales gracefully 🚀 #DataEngineering #ETL #DataQuality #DataTransformation #AnalyticsEngineering #DataGovernance #DataOps #Spark #dbt #Airflow

FastAPI’s dependency injection system is one of its most practical features for building scalable services. Instead of instantiating resources (like database sessions, clients, or auth logic) inside each endpoint, you define them once as dependencies and inject them wherever needed. Basically, FastAPI resolves these at runtime, handles lifecycles (setup/teardown), and supports async dependencies. Technical Problem: Traditional APIs suffer from tight coupling, parameter drilling through call stacks and limited control over dependency lifecycles. Global state breaks testability and manual resource management leads to connection leaks. Under the Hood: - Request Scoped Singleton Pattern: Each dependency is instantiated once per request, cached in the request state, and reused across the dependency graph - Lazy Evaluation: Dependencies aren't resolved until actually needed, optimizing cold start performance - DAG Resolution: FastAPI builds a Directed Acyclic Graph of dependencies at startup and resolves them in topological order - Generator Protocol: yield syntax enables deterministic resource cleanup via context managers, preventing connection leaks - Type Safe Overrides: The Depends() mechanism allows compile time type checking while enabling runtime dependency injection for testing Architectural Benefits: - Inversion of Control: Dependencies declare what they need, not how to get it - Separation of Concerns: Business logic isolated from infrastructure code - Testability: Override dependencies using app.dependency_overrides without mocking frameworks - Resource Management: Automatic cleanup via generator protocol prevents leaks - Performance: Per-request caching eliminates redundant database/auth calls #FastAPI #Python #DependencyInjection #SoftwareArchitecture #Microservices #AsyncPython #APIDesign #BackendEngineering

Your LLM pipeline chokes on PDFs, PowerPoints, and random HTML dumps. Unstructured.io fixes that. It's an open-source ETL framework that turns messy documents into clean, metadata-rich chunks your models can actually use. The flow is simple: Partition breaks docs into logical elements (titles, tables, lists) Inference adds layout ML for tables, headers, OCR Ingest CLI pulls from 40+ sources (S3, Gmail, Jira, filesystem) Output as JSON, Markdown, Arrow, or push straight to vector DBs One API. Python SDK, CLI, or Docker service. No stack rewrite required. If you're still writing custom parsers for every document type, this table will save you weeks. 🧠 Full breakdown on The Neural Blueprint: https://lnkd.in/d8ai3JEi #UnstructuredIO #DocumentETL #RAG #MLOps #LLMPipelines

I’ve been consistently practicing Data Structures & Algorithms, focusing on understanding the underlying logic and core patterns behind each problem rather than just solving them. Here’s a summary of some recent problems I’ve tackled, along with the key concepts learned 📌 225. Implement Stack using Queues Key Concept: Queue-based simulation of stack operations (using two queues or a single optimized queue) https://lnkd.in/gabQrA_R 📌232. Implement Queue using Stacks Key Concept: Stack-based implementation using two stacks — one for enqueue, one for dequeue operations https://lnkd.in/gwq44Akm 📌102. Binary Tree Level Order Traversal Key Concept: Breadth-First Search (BFS) using a queue for level-wise traversal of binary trees https://lnkd.in/gn4ejwNK 📌239. Sliding Window Maximum Key Concept: Deque-based sliding window to efficiently track the maximum element in each window https://lnkd.in/gwDAFZkP 📌435. Non-overlapping Intervals Key Concept: Sorting by end time + greedy interval selection to minimize overlaps https://lnkd.in/gVwP-2pf 📌1710. Maximum Units on a Truck Key Concept: Greedy approach inspired by the knapsack problem — maximize total units by sorting on value https://lnkd.in/gqxdifBu 📌646. Maximum Length of Pair Chain Key Concept: Similar to non-overlapping intervals — greedy selection based on the smallest end time https://lnkd.in/gik6pJ6K These problems helped me strengthen concepts like queue–stack interconversion, BFS traversal, sliding window optimization, greedy algorithms, and interval scheduling techniques. I’d highly recommend trying these problems out — they’re great for building pattern recognition and problem-solving intuition. Here’s my LeetCode Profile for reference: https://lnkd.in/gp38YMN7 #DSA #LeetCode #Java #Algorithms #ProblemSolving #CodingInterview  #SoftwareDevelopment #SDE #TechJourney #DailyCoding

Predictive Anomaly Detection for Data Center Assets! 🚀 Excited to share a project that proactively identifies hardware anomalies using simulated data center telemetry and powerful machine learning! This solution employs Isolation Forest and a Keras Autoencoder for effective anomaly detection, paving the way for better predictive maintenance. 💡 The project includes robust feature engineering (lag/rolling features) and clear training scripts. Quick Start pip install -r requirements.txt Run src/generate_synthetic.py, then src/preprocess.py, and finally src/train_and_evaluate.py. 🔗 GitHub Repository: https://lnkd.in/gmr_U9g5 🔗 Live Streamlit App: https://lnkd.in/gtRhuWKB Check it out and let me know your thoughts! 👇 #AnomalyDetection #MachineLearning #PredictiveMaintenance #DataCenter #Python

Ask HN: What are Your Strategies for Managing MCP Servers with Multiple AI Agents? https://lnkd.in/g-7iy3ZU Navigating the Challenges of AI Agent Development I've been actively building AI agents using LangChain, n8n, and custom Python scripts. In the process, I've encountered several challenges that many of you may relate to: MCP Server Configuration: Do you set up separate MCP servers for each agent, or do you share them across projects? Credentials and Config Management: What's your strategy for managing credentials and configurations across different frameworks? Dependency Conflicts: Have you faced issues where different agents require incompatible Python versions? Observability Needs: How do you keep track of which agent is utilizing a specific tool? To streamline this, I’ve started building a private registry to centralize configurations. However, I wonder if I’m overengineering or if these pain points resonate with you. Let’s connect! How are you addressing these challenges? Please share your insights below! Source link https://lnkd.in/g-7iy3ZU

I spent a weekend answering a question most ML portfolios ignore: what happens after the notebook? I had a dataset with 13 features and 6 model candidates. LightGBM won with the best balance - 1800€ RMSE, fast training, quick predictions. But that's where most projects end. I wanted to see what it actually takes to deploy one. So I built a complete vehicle pricing application. - FastAPI backend with PostgreSQL. - Streamlit frontend where users can add vehicles, get predictions, and retrain the model with new data. - Everything containerized with Docker. - Zero license costs. Here's what surprised me: the model selection was the easy part. 😯 The real challenge? Figuring out how to store and retrieve preprocessing artifacts. When a user inputs a new vehicle, the app needs to apply the exact same Box-Cox transformations and scaling that the training data received. Get that wrong and your predictions become garbage. I ended up building a two-table architecture - a 'bronze' table for raw CRUD operations and a 'gold' table with preprocessed data ready for the model. That separation made retraining possible without breaking the API. Most portfolio projects showcase "I trained a model and got X% accuracy." This one forced me to think about database design, API architecture, preprocessing pipelines, and how all those pieces talk to each other. It took 48(ish) hours. It taught me more about production ML than any certification. When you're assessing talent, what tells you someone can actually deliver beyond the Jupyter notebook? Full code and architecture in the first comment #MachineLearning #DataEngineering