Kidong Lee’s Post

Nowadays, there are main topics around iceberg like how to ingest streaming and files to iceberg, how to transform iceberg tables and how to query iceberg tables. The operations of ingestion and transformation of iceberg may be insert, update or delete, which need to be managed like automated iceberg table maintenance controlled by distributed lock. All the operations of data ingestion, transformation and exploration of iceberg need to be controlled by storage security based on RBAC with catalog, schema and table level. Let's see the below picture how to ingest streaming events and files, transform and explore iceberg tables with strong storage security and iceberg table management like automated iceberg table maintenance and distributed lock in Chango( https://lnkd.in/gHvp6Eud ). - Streaming events will be ingested into iceberg through REST without additional streaming infra like streaming platform and streaming jobs. - External files like CSV, JSON, Parquet and ORC will be ingested to iceberg through SQL without additional batch infra like spark cluster. - Iceberg tables will be transformed with ETL query jobs which are integrated with Chango Query Exec through REST without additional setup of build tools. - Iceberg tables will be explored with strong storage security. As seen, Chango is All-in-One solution for building iceberg centric data lakehouses.

🚀 Boost Your Apache Spark Game! Struggling to maximize Spark performance? Dive into this in-depth guide on Adaptive Query Execution (AQE) — Spark's game-changing feature for dynamic query optimization. Learn how AQE can fine-tune shuffle partitions, manage skewed data, and auto-optimize join strategies for blazing-fast data processing. Perfect for data engineers and analytics pros looking to unlock Spark's full potential. 👉 Read now and supercharge your queries! https://lnkd.in/gjqgby4h #DataEngineering #BigData #ApacheSpark #AQE #PerformanceOptimization

"This solution allows the parsing of your nested JSON payload to struct along with schema evolution without any restarts." Irfan Elahi introduces a seamless workflow for parsing nested JSON and schema evolution in Delta Lake Tables.

Apache Iceberg is revolutionizing data lakes, but we need to cut through the hype and talk about the real technical challenges of optimal data ingestion: 1. Optimizing File Size & Layout > Small file problem leads to metadata bloat > Suboptimal compaction degrades query performance > Challenge: Balancing write latency vs. read performance 2. Schema Evolution Complexity > Adding/modifying columns while maintaining backward compatibility > Handling nested data structures > Ensuring schema changes don't break downstream consumers 3. Time Travel & Versioning > Managing snapshot state without performance penalties > Implementing ACID transactions at scale > Garbage collection without impacting active queries 4. Performance Bottlenecks > Write amplification during updates/deletes > Partition evolution as data patterns change > Metadata handling at scale (millions of files) 🔍 Real-world Challenge: Ingesting real-time streaming data into Iceberg while maintaining optimal file sizes, ensuring exactly-once semantics, and preserving low latency has been a significant hurdle for many organizations. 💡 Enter Estuary Flow: The Solution Estuary Flow addresses these challenges head-on: - Real-time data integration with sub-second latency - Automated schema evolution handling - Private deployment options for enhanced security - Native integration with Iceberg for seamless data flow - Intelligent auto-compaction for optimal file sizes (soon!) For enterprises requiring ironclad security and control, Estuary Flow's private deployment ensures your data never leaves your infrastructure while still leveraging all the benefits of Iceberg. Apache Iceberg is the future of data lakes, but optimal implementation requires solving complex ingestion challenges. Estuary Flow turns these challenges into opportunities, enabling real-time, secure, and optimized data integration. Check out our end-to-end guide for #ApacheIceberg data integration: https://lnkd.in/d8KGP5yK

🌟 Unlock Real-Time processing capabilities in spark 🌟 Let us dive deep into the exciting world of real-time data processing with Apache Spark. 🔵 What is Real-Time Processing? ➡ Real-time processing involves the continuous input, processing, and output of data which allows end users to make decisions in quick based on the latest information. ________________________________________________________________________________ 🔵How Does Real-Time Processing Differ from Batch Processing? ➡Real-time processing deals with data instantly as it arrives whereas batch processing data is handled in periodic batches. _________________________________________________________________________________ 🔵How Does Spark Support Real-Time Processing? ➡Spark has built-in native API that supports stream processing. ➡Data can be ingested from various sources like Kafka, Flume, and Kinesis and can apply transformations over it. ➡Spark provides unified approach of dealing with both streaming and batch processing. #dataengineering #spark #databricks #dataanalysis

🚀 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐒𝐩𝐚𝐫𝐤'𝐬 𝐂𝐚𝐭𝐚𝐥𝐲𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫 🚀 In the realm of big data processing with Apache Spark, efficiency is the name of the game. And at the heart of Spark's performance prowess lies the Catalyst optimizer, a sophisticated engine that transforms and optimizes query execution plans, ensuring lightning-fast data processing. Structured APIs shine brightly in the Spark ecosystem, thanks to the Catalyst optimizer. Here's a glimpse into how it works its magic: 1️⃣ 𝗣𝗮𝗿𝘀𝗲𝗱 𝗟𝗼𝗴𝗶𝗰𝗮𝗹 𝗣𝗹𝗮𝗻 (Unresolved): Ensures query syntax is error-free. 2️⃣ 𝐑𝐞𝐬𝐨𝐥𝐯𝐞𝐝/𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝐝 𝐋𝐨𝐠𝐢𝐜𝐚𝐥 𝐏𝐥𝐚𝐧: This resolves the query and checks table names, column names, etc., in your query against the catalog. 3️⃣ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐋𝐨𝐠𝐢𝐜𝐚𝐥 𝐏𝐥𝐚𝐧: Now, the real magic unfolds. The Catalyst optimizer steps in to optimize the logical plan using a myriad of rules – from filter pushdown to projection combining. It's like having a seasoned architect redesigning our blueprint for maximum efficiency. 4️⃣ 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐏𝐥𝐚𝐧: With the optimized logical plan in hand, Spark generates multiple physical plans to execute our query, each with its own set of advantages and trade-offs. 5️⃣ 𝐂𝐨𝐬𝐭 𝐌𝐨𝐝𝐞𝐥: The optimizer evaluates the cost of each physical plan and selects the one that promises the best performance. 6️⃣ 𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Finally, the chosen physical plan undergoes a transformation into low-level API RDD code, ready to be executed with precision and speed. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐑𝐃𝐃𝐬, 𝐲𝐨𝐮 𝐚𝐬𝐤? Well, if you're working with RDDs at the low-level API, you bypass the Catalyst optimizer entirely. While RDDs offer flexibility, they miss out on the optimization prowess of Catalyst, potentially sacrificing performance in the process. Many thanks to Sumit Mittal sir for his precise and lucid explanation of important Spark topics! #DataEngineering #ApacheSpark #Optimization #BigData #CatalystOptimizer

🚀 𝐒𝐩𝐚𝐫𝐤 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐑𝐞𝐚𝐥-𝐋𝐢𝐟𝐞 𝐓𝐨𝐭𝐚𝐥 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 100𝐓𝐁 → ~?!🚀 When working with large-scale data pipelines, efficient storage and faster processing are game-changers. This is where data compression in Apache Spark comes into play. Here’s how I approach compression in real-world Spark projects: ✅ 𝐏𝐚𝐫𝐪𝐮𝐞𝐭 𝐨𝐫 𝐎𝐑𝐂 𝐟𝐨𝐫 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 These columnar formats are ideal for analytical workloads. Compression comes baked in, with support for algorithms like Snappy, Zlib, or Gzip. I often prefer Snappy for its balance between compression speed and file size. ✅ 𝐊𝐚𝐟𝐤𝐚 𝐚𝐧𝐝 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 When integrating Spark Streaming with Kafka, enabling message compression (e.g., LZ4) can significantly reduce network overhead without compromising processing speed. ✅ 𝐒𝐩𝐚𝐫𝐤 𝐒𝐡𝐮𝐟𝐟𝐥𝐞 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 For distributed processing, enabling spark.shuffle.compress and spark.shuffle.spill.compress ensures intermediate data is compressed, reducing disk and memory usage during shuffle operations. ✅ 𝐃𝐞𝐥𝐭𝐚 𝐋𝐚𝐤𝐞 Compression techniques like ZSTD (Zstandard) can further enhance Delta Lake's performance, especially when dealing with incremental data loads. Real-life Example: In a recent project, enabling Snappy compression for Parquet files reduced storage costs by 30%, while tuning shuffle compression sped up join operations by 20%. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Choosing the right compression method balances storage, processing speed, and resource efficiency. Always test for your specific workload before finalizing! What are your go-to compression strategies in Spark? Let’s discuss this in the comments! 👇 #BigData #ApacheSpark #DataEngineering #DataCompression #CloudComputing #StorageOptimization

Let's Learn Data Engineering Together! #Day64ofSparkDataEngineering Caching and Persistence ( cache() and persist() Transformation): ---> The Two important techniques such as "cache()" and "persist()" allows spark to store some or all of the data in the memory or on the disk so that it can be reused without recomputing the dataframe again . ---> By caching and persisting DataFrames , you can avoid recomputing the results when they are needed again in later stages ---> Caching and Persistence are lazy operations in spark which means that they do not take effect until an action is performed on them. R GANESH Sagar Prajapati Amulya A. Ankit Rai Komal Khakal Shivakiran Kotur Arabinda Mohapatra Abhisek Sahu

🔄 Streams: A Gateway to Efficient Data Handling and Transformation 🔄 In today's data-driven world, handling and transforming large volumes of data efficiently is crucial. This is where streams come into play, offering a powerful solution for real-time data processing. Why Streams? 1. Memory Efficiency: Streams allow you to process data piece by piece, making it ideal for large datasets that can’t fit entirely in memory. 2. Performance: By processing data as it arrives, streams reduce latency and improve overall performance. 3. Flexibility: Streams support various data sources and sinks, providing versatility in handling different data formats and destinations. Key Benefits: - Real-Time Processing: Handle data as it comes in, which is essential for applications requiring immediate insights and actions. - Resource Optimization: Streams minimize memory usage, leading to more efficient resource utilization. - Scalability: Easily scale up your data processing capabilities to meet growing demands. How to Use Streams: 1. Identify the Data Source: Determine where your data is coming from – it could be files, databases, or real-time feeds. 2. Choose the Right Tools: Utilize libraries and frameworks that support streaming, such as Apache Kafka, Flink, or even built-in language features like Java Streams. 3. Define Transformation Logic: Specify how the data should be processed and transformed as it flows through the stream. 4. Handle Data Efficiently: Implement mechanisms for error handling, buffering, and backpressure to ensure smooth data flow. By leveraging streams, you can unlock new levels of efficiency and responsiveness in your data handling and transformation processes. Ready to streamline your data workflows? Let's dive into the world of streams! 🌐💡 #DataProcessing #Streams #RealTimeData #BigData #TechInnovation

In order to deepen my understanding of systems infrastructure, I have committed to reading one chapter of Martin Kleppmann's seminal text 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮-𝗜𝗻𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 📕 each day. Today, let's explore the key insights from Chapter 10: 𝗕𝗮𝘁𝗰𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴. 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 💡 Batch processing is a model for handling large-scale data by transforming entire datasets in bulk, rather than on a per-transaction basis. It’s particularly useful for big data applications, where you can trade off low-latency for higher throughput and resource efficiency. 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄 𝗮𝗻𝗱 𝗙𝗮𝘂𝗹𝘁 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲 🔄 In batch processing, data flows through a series of transformations, with each stage applied to the whole dataset and outputs saved at every step to ensure fault tolerance. If a stage fails, only that stage needs to be rerun rather than restarting the entire process. Systems like MapReduce and Spark handle this well by supporting resilient distributed datasets (RDDs), which automatically manage fault recovery. 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 ⚙️ To make processing more efficient, datasets are partitioned and processed in parallel across nodes. Partitioning divides the workload, and parallelism executes tasks simultaneously, cutting down on processing time. Proper partitioning is important for balancing the load and avoiding bottlenecks. 𝗕𝗮𝘁𝗰𝗵 𝗮𝗻𝗱 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 🌊 Batch processing works well for periodic updates and analysis, but for real-time data processing, stream processing is more suitable. Stream processing frameworks like Apache Flink and Apache Kafka allow data to be processed as it arrives. Combining both approaches can work well when you need both historical and real-time data handling. There you have it for my summary of Chapter 10. If you enjoyed this post and would like to see more, please consider following me. I'd love to connect with others interested in distributed systems and data engineering. 😃 Stay tuned for my summary of Chapter 11 tomorrow! #DataEngineering #DistributedSystems #SystemDesign #BatchProcessing #BigData #DataArchitecture #TechLearning