Challenges in building Iceberg tables from Kafka data

Ever wondered why Kafka is insanely fast at moving data? It’s all about zero-copy writes. Normally, when data moves from the network to the disk, it bounces between user space and kernel space multiple times, that’s a lot of wasted movement. But Kafka skips all that using the `sendfile` system call, which lets the OS directly move data from disk (or page cache) to the network socket. Fewer copies → faster throughput. In short, Kafka avoids passing data around like a hot potato. Fun fact, `sendfile` doesn’t work with TLS/SSL since encryption needs to happen in user space, so it only shines in plain transfers. I actually used this same zero-copy technique while working on the Remote Shuffle Service for Apache Spark, and the speedup was no joke. Which other system-level optimizations have amazed you lately? Drop your favorites below 👇 #Kafka #ZeroCopy #SystemDesign #Performance #Engineering #LearnWithNayan Credits: https://lnkd.in/dtVdnbGV

One of the hidden gems that I have is my video course on Kafka Connect. I think many underestimate its power. Kafka Connect lets you stream data between Kafka and almost any external system without writing a single line of code. That means real-time syncing with zero custom code. A new change in the source database can be instantly reflected in your destination system! I think this is a super powerful and convenient feature. https://lnkd.in/e6qfGKQy #Kafka #KafkaConnect

New blog post on why I’m not a fan of zero-copy Iceberg tables for Apache Kafka. “Zero-copy” integration between Apache Kafka and Iceberg has been discussed a lot online recently. The idea is that Kafka topics live directly as Iceberg tables. It’s an appealing vision: one copy of data shared between stream processing and analytics. But from a systems design perspective, it creates a tightly coupled system that’s harder to operate and less efficient: 🔹 Kafka brokers take on heavy compute work building and reading Parquet files. 🔹 Optimizing for analytics breaks Kafka’s sequential read efficiency, and vice versa. 🔹 Schema evolution becomes a tug-of-war between fidelity and cleanliness. 🔹 Boundaries blur. Who owns the data, Kafka or the lakehouse? Sometimes, duplication is cheaper than coupling. 👉 Full post: https://lnkd.in/dEt2AfEu

"Zero-copy" between Kafka and Iceberg is one of those ideas which seem intriguing on the surface, but if you think more about it, there's actually quite a few issues with it. Access patterns--which heavily impact how data should be laid out for efficiency--differ substantially between a log (Kafka) and analytics store (Iceberg). Trying to satisfy both use cases with a single instance of your data is going to yield a sub-optimal system. Great post by Jack Vanlightly.

🚀 Demystifying Apache Spark Architecture Apache Spark remains one of the most powerful frameworks for distributed data processing — but understanding its core architecture helps you truly unlock its performance potential. This simple visualization breaks it down: Driver: Coordinates the job, builds the DAG, and schedules tasks. Cluster Manager: Allocates resources across the cluster (YARN / Mesos / Standalone / Kubernetes). Executors: Run the actual tasks, cache data, and report results back to the driver. ✨ Together, these components make Spark incredibly scalable, fault-tolerant, and lightning-fast for large-scale analytics and ML workloads. 💬 How are you using Spark in your data pipelines today — for ETL, ML, or streaming? #DataEngineering #ApacheSpark #BigData #ETL #PySpark #DataScience #Analytics #CloudComputing #DistributedSystems

Kafka Series – Part 4: Kafka’s Core Design Principles Kafka isn’t “just fast” by accident — it’s designed with a few core principles that make it incredibly reliable and scalable. Let’s break them down clearly 👇 🧱 1. Durability - Kafka persists data to disk immediately. - Uses a commit log structure for sequential writes (very fast). - Data survives restarts and failures — critical for financial or critical systems. 🌐 2. Scalability - Topics can be split into many partitions and distributed across multiple brokers. - You can scale producers, consumers, and brokers independently. - Horizontal scaling makes it ideal for growing data volumes. 🧭 3. Fault Tolerance - Data is replicated to multiple brokers. - If one broker fails, replicas take over automatically. - No single point of failure when configured correctly. 🚀 4. High Throughput & Low Latency - Sequential disk I/O, zero-copy transfer, batching, and compression. - Capable of handling millions of messages/sec with millisecond latency. These principles are why Kafka became the backbone of real-time data platforms worldwide. 💡 If you feel I missed something important or explained it differently than you would, please drop your thoughts in the comments — I’d love to learn from your perspective too. #Kafka #SystemDesign #ScalableArchitecture #FaultTolerance #RealTimeData #LearnTogether

🚀 Open-Source Powerhouses Behind Modern Data Engineering When people talk about data, they often think of dashboards and insights. But behind every clean dashboard, there’s a set of powerful open-source tools working quietly to move, process, and orchestrate data. Let’s start with some of the key Apache projects every Data Engineer should know 👇 ⚙️ Apache Airflow – orchestrates complex data pipelines. ⚡ Apache Kafka – handles real-time data streaming. 🔥 Apache Spark – processes huge datasets in parallel. 🌊 Apache Flink – brings real-time analytics to life. 🔄 Apache NiFi – automates and manages data flows. 🏗️ Apache Hive – enables SQL-like queries on big data. These open-source tools are the backbone of modern data platforms. In my next posts, I’ll break them down one by one — showing how they work together to turn raw data into real insight. #DataEngineering #Apache #Airflow #Kafka #Spark #Flink #NiFi #OpenSource

💡 How does Spark manage memory internally? Apache Spark’s performance heavily depends on efficient memory management — it determines how data is cached, shuffled, and processed across executors. Inside each JVM, Spark divides memory into four key regions: * Execution Memory: Temporary workspace for joins, aggregations, and shuffles. * Storage Memory: Used for caching and persisting RDDs/DataFrames. * User Memory: Holds user-defined objects and internal metadata. * Reserved Memory: Small portion reserved to prevent out-of-memory crashes. Spark’s Unified Memory Model allows Execution and Storage to share space dynamically — unused cache memory can be borrowed for computation and vice versa. A well-tuned memory configuration ensures minimal spills to disk and faster job execution. Mastering this balance is the key to unlocking true Spark performance. #ApacheSpark #BigData #DataEngineering #SparkOptimization #MemoryManagement #DataProcessing #ETL #PerformanceTuning #Databricks #CloudComputing #DataAnalytics #DistributedComputing #SparkJobs #DataScience #ClusterComputing #DataEngineer #TechArchitecture #DataOps #BigDataFramework #ModernDataStack

I recently explored Apache Kafka, and it’s been a fascinating look into how real-time data streaming actually works behind the scenes. ➜ Why Kafka exists - understanding the problems it was built to solve and why it’s such a core piece of modern data infrastructure ➜ Inside the architecture - how topics, producers, consumers, partitions, and brokers all fit together to create a scalable, fault-tolerant system ➜ Making sense of data flow - learning how offsets, message keys, and consumer groups keep messages ordered and systems reliable ➜ Practical demonstrations - explored examples showing real-time message flow, partitioning, multiple consumer groups, and a small Python-based project that sent and received emails through Kafka ➜ Beyond the basics - a glimpse into Kafka Connect and Kafka Streams, and how they extend Kafka’s power into data integration and stream processing It’s incredible how systems like Kafka make real-time data pipelines possible at scale. This deep dive definitely helped me appreciate the design and thinking behind distributed systems even more. #ApacheKafka #DataEngineering #BigData #EventStreaming #RealTimeData #KafkaStreams #KafkaConnect #DistributedSystems #TechLearning #CareerGrowth

Mastering Apache Kafka – From Basics to Performance Optimization! If you’ve ever worked with real-time data, event-driven systems, or streaming pipelines, you’ve probably heard of Apache Kafka. I’ve compiled a complete beginner-to-advanced guide with concepts, examples, and performance tuning tips to help you become Kafka-ready: 🔹 Kafka Basics – Topics, Partitions, Replication, Brokers, Leaders & Consumer Groups 🔹 Example Use Cases – Website tracking, real-time stream processing, log aggregation, event sourcing 🔹 Producers & Consumers – Ack values, batching, compression & client libraries 🔹 Performance Optimization – Tuning brokers, balancing partitions, ISR (In-Sync Replicas), retention policies 🔹 Kafka Architecture Deep Dive – Logs, offsets, Zoo Keeper, producer/consumer APIs 🔹 Best Practices – Partition distribution, avoiding hardcoding, scaling strategies, server concepts 💡 Whether you’re just starting with Kafka or looking to optimize production systems, this guide gives you a clear roadmap from basics ➝ advanced performance tuning. 👉 Check it out for complete notes & hands-on practices 😁 🧐 👍 : https://lnkd.in/gyjskYZN #ApacheKafka #Kafka #EventStreaming #BigData #DataEngineering #RealTimeData #LearningCommunity #HelpingHands #AnshLibrary