Estuary’s Post

🚀 Exploring Beyond JSON: 5 Advanced Data Serialization Formats for Your Next Project 🚀 In an era where data is increasingly becoming the backbone of applications across industries, the choice of data serialization formats can significantly impact the performance and scalability of your systems. Dr. Ashish Bamania highlighted some compelling alternatives to JSON that promise to enhance data handling capabilities in his enlightening article on Medium. ❓ Why Move Beyond JSON? While JSON has been the de facto standard due to its simplicity and ease of use, it isn't always the most efficient or fastest method, especially for high-performance applications. Exploring more efficient data serialization formats can lead to improved processing speeds, reduced bandwidth usage, and enhanced data integrity. ⚙️ Top 5 Serialization Formats to Consider: 🔹 Cap’n Proto: Dubbed as "Infinity times faster," it's designed for incredibly fast data exchange and minimal parsing overhead. 🔹 Protocol Buffers: Developed by Google, this format is not only lighter on resources but also provides a more structured schema for data interchange. 🔹 Avro: Favoured in the Hadoop ecosystem, Avro supports schema evolution, allowing serialized data formats to be easily updated. 🔹 MessagePack: Known for its high efficiency and compact size, it's ideal for applications that require data caching and real-time communication. 🔹 Thrift: Created by Apache, it supports multiple programming languages and is used for defining and creating services for numerous applications. 🎯 Strategic Importance: 🔹 Performance Optimization: These formats can drastically reduce the time and computational resources required to parse large volumes of data. 🔹 Cross-Language Support: Most of these formats offer better support across various programming environments, enhancing compatibility. 🔹 Future-Proof: With features like schema evolution and multiple data encoding options, these formats are built to handle future technological advancements. As the volume and complexity of data grow, choosing the right serialization format becomes crucial. By considering these advanced options, developers can ensure that their applications are not only ready for today's challenges but also equipped for future demands. 👉 https://lnkd.in/dNnxgzcY 👥 Let’s discuss: 🔹 How can your projects benefit from adopting one of these advanced serialization formats? 🔹 What challenges might arise when transitioning from JSON to another serialization method? 🔹 How do these formats align with the needs of modern, data-intensive applications in your field? #DataScience #Programming #TechnologyInnovation #BigData #SoftwareDevelopment

Constraints are important tools in a data platform to maintain data quality and enforce data integrity. Most databases allow configuring constraints on tables to improve the quality and reliability of the data. Delta Lake supports two types of constraints 🔒 Enforced constraints ℹ️ Non-enforced informational constraints 🔒 NOT NULL and CHECK constraints are enforced and automatically ensure data integrity in a delta table resulting in a failed transaction on violation. ℹ️ Primary Key and Foreign Key constraints are unenforced and are to be used with caution as they do not automatically fail on violation. To learn more with examples check the latest edition of my Data Engineering Newsletter What's on the list? ⭐Cloud News ✨Databricks system tables GA ✨Delta UniForm Iceberg ⭐Constraints in Delta Lake. Subscribe and stay ahead of the curve. #urbandataengineer #edition11

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

The data lake revolution continues, but getting data in — #ingestion — remains a critical hurdle. Most vendors are just doing the minimum to support the open table format (just write the file). This article explores the top vendors who are simplifying data lake ingestion while supporting modern table formats like #ApacheIceberg, #ApacheHudi, and #DeltaLake with advanced table optimization services (clustering, cleaning, file resizing, and compaction services that provide 10x query performance over "just writing the file" query performance). #datalakehouse

As data continues to grow exponentially, organizations are constantly seeking new ways to manage and extract value from their information assets. One powerful tool that is gaining increasing attention in the data engineering community is Apache Iceberg. This open-source table format offers several key advantages that can greatly benefit data teams. Every one asks if it can be leveraged as a powerful data lake , The answer is yes. Iceberg's design and features make it well-suited for use as the underlying table format for a data lake architecture. The key advantages that enable this include: 1. Scalable Metadata Management: As mentioned, Iceberg stores table metadata separately from the actual data files. This allows the metadata to be managed independently and scaled as needed to handle the massive volumes of data typically found in a data lake. 2. Schema Flexibility: Iceberg supports schema evolution, allowing tables to be updated with new columns or modified without breaking existing queries and pipelines. This is crucial for the ever-changing nature of a data lake. 3. Time Travel Capabilities: The ability to query historical versions of data is invaluable in a data lake setting, where data is constantly being added and updated. Users can easily investigate issues or restore previous states as needed. 4. Performance Optimizations: Iceberg includes features like partition pruning and data file filtering that improve query performance, even on the large, unstructured datasets common in a data lake. 5. Open Format: As an open-source project, Iceberg is compatible with a wide range of data processing engines and can integrate seamlessly with other data lake components. As a data engineer , does this work for you ? Share your thoughts on the tools you are using for lake house storage . #dataengineering #datalake #ETL

I recently finished reading “Designing Data Intensive Applications” this summer and wanted to share the most illuminating points I took away from the textbook. I highly recommend going through the whole 611 pages as well if you want to level up your system design skills! Replication: Figuring out how to scale horizontally is one of the hardest tasks in system architecture. Single leader synchronous replication ensures no replication lag, but asynchronous replication can still process writes if any of the follower nodes go down. Sharding & Partitioning: Sharding splits your data amongst different nodes to increase throughput. You can shard based on key-range (allowing for efficient range queries) or by a hash of the key to better avoid hot spots. Rebalancing and changing the number of nodes is required as your DB grows/shrinks, and you should keep track of which nodes are in charge of which partitions using ZooKeeper or another service. Consistency, Serializability: In distributed systems it’s hard to get out of the “eventually consistent” model. If you’re able to compromise on this, your system design can handle a lot more throughput and scale better generally. Full serializability also comes at a heavy performance cost. Batch Jobs and Stream Processing: Stream processing (like with Apache Kafka), can be thought of as a batch job with an infinite size. In general, batch jobs are more fault-tolerant (because they can be re-ran on the same data on failure), while stream processing could lose data on crashes if the producer or message broker does not buffer data. All in all, I learned that there’s no silver bullet when it comes to designing a data system. Tradeoffs will have to be made on throughput, robustness and consistency. A great engineer is able to take these points into consideration and build the architecture best suited for the business requirements.

In data engineering, separating compute and storage has become essential for building scalable and efficient data platform. Here’s why: Scalability: Independently scale compute and storage to meet specific needs, ensuring optimal performance as data volumes grow. Cost Efficiency: Allocate compute resources only when necessary, reducing idle time and cutting costs. Flexibility: Use the best tools for each task, whether it’s batch processing, real-time analytics, or machine learning, without being tied to a single platform. Resilience: Maintain data integrity even if compute resources fail, allowing for quick recovery and uninterrupted workflows. Open Table Formats: Leverage open table formats like Apache Iceberg, Delta Lake, or Apache Hudi for better data management and interoperability. These formats support ACID transactions, schema evolution, and versioning, ensuring consistent and high-performance data operations across various tools and platforms. No Data Movement: With this architecture, there’s no need to transfer data between systems/clouds for processing or analysis. Compute engines can directly access and process data where it resides, whether it’s in a data lake or an object store. This eliminates unnecessary data movement, reducing latency and improving efficiency. The Lakehouse architecture fully embraces this separation, combining the flexibility of data lakes with the performance of data warehouses. By leveraging scalable object storage, powerful on-demand compute engines, open table formats, and the ability to process data in place, Lakehouse enables advanced analytics across both structured and unstructured data. #DataEngineering #LakehouseArchitecture #ComputeAndStorage #Scalability #OpenTableFormats

📜 How Kubernetes Stores Data in etcd 📜 Kubernetes stores all its cluster resources (Pods, Services, Deployments, ConfigMaps, etc.) in etcd. Etcd is a distributed, fault-tolerant key-value store that serves as the source of truth for the entire cluster. ⚡ Protocol Buffers (Protobuf): etcd uses Protocol Buffers (protobuf) exclusively for data serialization. All Kubernetes resources are stored as protobuf data in etcd, ensuring compact and efficient storage. ⚡ Key Format in etcd: The keys in etcd are structured based on resource types and their scope: 🔹 Namespace-scoped resources (like Pods, Deployments, etc.) are stored as: /registry/{resource-type}/namespace/{resource-name} 🔹 Cluster-scoped resources (like ClusterRoles) are stored as: /registry/{resource-type}/{resource-name} 🔹 Custom Resource Definitions (CRD): For CRDs, the key format is: /registry/apiextensions. k8s. io/customresourcedefinitions/{crd-name} 🔄 Kubectl and the API Server: When we use kubectl or interact with the Kubernetes API, the API server is responsible for storing resources in etcd as protobuf data. It serializes and sends back the resource data in JSON format when responding to our requests. Here's how the etcd keys look like in my local "kind" cluster

𝐀𝐩𝐚𝐜𝐡𝐞 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 ... [𝐏𝐚𝐫𝐭 2] Link to Part 1 in comments. ✅ 𝐂𝐨𝐦𝐦𝐨𝐧 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 Apache Iceberg is designed to address several common use cases in data lakes, providing advanced capabilities for handling large-scale data efficiently. Let's have a look at some of the very common use cases (refer AWS blog): ▪𝑫𝒂𝒕𝒂 𝑷𝒓𝒊𝒗𝒂𝒄𝒚 𝒂𝒏𝒅 𝑪𝒐𝒎𝒑𝒍𝒊𝒂𝒏𝒄𝒆 Iceberg supports 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐭 𝐝𝐞𝐥𝐞𝐭𝐢𝐨𝐧𝐬 required for compliance with data privacy laws such as GDPR and CCPA. This ensures sensitive information can be removed from datasets without significant overhead. ▪𝑹𝒆𝒄𝒐𝒓𝒅-𝑳𝒆𝒗𝒆𝒍 𝑼𝒑𝒅𝒂𝒕𝒆𝒔 Useful for datasets that 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐟𝐭𝐞𝐫 𝐢𝐧𝐢𝐭𝐢𝐚𝐥 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧, such as sales data that might change due to returns or adjustments. Iceberg allows for 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵 𝘳𝘦𝘤𝘰𝘳𝘥-𝘭𝘦𝘷𝘦𝘭 𝘶𝘱𝘥𝘢𝘵𝘦𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘵𝘩𝘦 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘳𝘦𝘸𝘳𝘪𝘵𝘦 𝘦𝘯𝘵𝘪𝘳𝘦 𝘱𝘢𝘳𝘵𝘪𝘵𝘪𝘰𝘯𝘴. ▪𝑺𝒍𝒐𝒘𝒍𝒚 𝑪𝒉𝒂𝒏𝒈𝒊𝒏𝒈 𝑫𝒊𝒎𝒆𝒏𝒔𝒊𝒐𝒏𝒔 Ideal for SCD tables, where data changes occur at unpredictable intervals. For example, customer records that may change over time (e.g., address updates). ▪𝑨𝑪𝑰𝑫 𝑻𝒓𝒂𝒏𝒔𝒂𝒄𝒕𝒊𝒐𝒏𝒔 Supports ACID transactions, ensuring 𝐝𝐚𝐭𝐚 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲, 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲, & 𝐝𝐮𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲. This is crucial for scenarios that require transactional integrity, such as financial data processing. ▪𝑻𝒊𝒎𝒆 𝑻𝒓𝒂𝒗𝒆𝒍 𝒂𝒏𝒅 𝑯𝒊𝒔𝒕𝒐𝒓𝒊𝒄𝒂𝒍 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 Enables 𝐪𝐮𝐞𝐫𝐲𝐢𝐧𝐠 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 for trend analysis, auditing, or rollback to previous states. This is beneficial for 𝘵𝘳𝘢𝘤𝘬𝘪𝘯𝘨 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘰𝘷𝘦𝘳 𝘵𝘪𝘮𝘦 𝘢𝘯𝘥 𝘤𝘰𝘳𝘳𝘦𝘤𝘵𝘪𝘯𝘨 𝘪𝘴𝘴𝘶𝘦𝘴. ▪𝑬𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝑫𝒂𝒕𝒂 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 ➖ Facilitates 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐩𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧 𝐬𝐜𝐡𝐞𝐦𝐞𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐫𝐞𝐰𝐫𝐢𝐭𝐞𝐬, allowing more flexible and efficient data organization. ➖ Advanced data pruning and indexing mechanisms 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐪𝐮𝐞𝐫𝐲 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 by minimizing the data scanned. ▪𝑫𝒂𝒕𝒂 𝑳𝒂𝒌𝒆𝒉𝒐𝒖𝒔𝒆 𝑨𝒓𝒄𝒉𝒊𝒕𝒆𝒄𝒕𝒖𝒓𝒆 Supports modern data lakehouse architectures by providing a 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫𝐦𝐚𝐭 that can be used across various compute engines like Apache Spark, Apache Flink, Trino, and others. ▪𝑺𝒕𝒓𝒆𝒂𝒎𝒊𝒏𝒈 𝑫𝒂𝒕𝒂 𝑰𝒏𝒈𝒆𝒔𝒕𝒊𝒐𝒏 Can be used for streaming data ingestion where real-time data is continually appended and queried. ▪𝑰𝒏𝒕𝒆𝒓𝒐𝒑𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 Ensures 𝐜𝐨𝐦𝐩𝐚𝐭𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐛𝐢𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐞𝐧𝐠𝐢𝐧𝐞𝐬, facilitating diverse analytics and processing needs. Next: Iceberg Architecture ...𝓽𝓸 𝓫𝓮 𝓬𝓸𝓷𝓽𝓲𝓷𝓾𝓮𝓭 Follow Ashutosh Kumar for no-nonse content on #datengineering and #softwareengineering. #apacheiceberg #dataengineering #learningandsharing

🚀 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞: 𝐀𝐩𝐚𝐜𝐡𝐞 𝐈𝐜𝐞𝐛𝐞𝐫𝐠🚀 ✅ 𝐖𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐀𝐩𝐚𝐜𝐡𝐞 𝐈𝐜𝐞𝐛𝐞𝐫𝐠? 🔺 It is a table format created in 2017 by Netflix’s Ryan Blue and Daniel Weeks, for massive analytic datasets. 🔺 It overcame challenges with performance, consistency, and many of the challenges previously stated with the Hive table format. 🔺 Became open source in 2018. ✅ 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐢𝐭𝐬 𝐤𝐞𝐲 𝐟𝐞𝐚𝐮𝐫𝐞𝐬? 🔺 𝑺𝒄𝒉𝒆𝒎𝒂 𝑬𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝒂𝒏𝒅 𝑽𝒆𝒓𝒔𝒊𝒐𝒏𝒊𝒏𝒈 Iceberg supports schema evolution, allowing changes like adding, dropping, renaming columns, and updating column types without affecting query results or data consistency. It also provides versioning, enabling rollback to previous states. 🔺 𝑷𝒂𝒓𝒕𝒊𝒕𝒊𝒐𝒏𝒊𝒏𝒈 Iceberg offers hidden partitioning that abstracts the complexity, optimizing query performance by automatically selecting the most efficient partitioning strategy based on the query workload. 🔺 𝑨𝒕𝒐𝒎𝒊𝒄𝒊𝒕𝒚 𝒂𝒏𝒅 𝑪𝒐𝒏𝒔𝒊𝒔𝒕𝒆𝒏𝒄𝒚 Iceberg guarantees atomic operations and consistent reads through its design, which includes atomic commit protocols. This ensures that updates are all-or-nothing, preventing partial writes and maintaining data integrity. 🔺 𝑫𝒂𝒕𝒂 𝑳𝒂𝒚𝒐𝒖𝒕 𝒂𝒏𝒅 𝑰𝒏𝒅𝒆𝒙𝒊𝒏𝒈 Iceberg optimizes data layout and includes built-in indexing mechanisms, such as manifest files and metadata trees, which enhance query performance by pruning unnecessary data reads. 🔺 𝑻𝒊𝒎𝒆 𝑻𝒓𝒂𝒗𝒆𝒍 Iceberg supports time travel, allowing users to query data as it existed at any point in time. This facilitates easy analysis of historical data and recovery from accidental changes. 🔺 𝑰𝒏𝒕𝒆𝒓𝒐𝒑𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 Iceberg is designed to be compatible with multiple processing engines such as Apache Spark, Apache Flink, and Trino, making it versatile and easy to integrate into existing data infrastructure. ✅ 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Iceberg is designed to handle massive tables, often containing tens of petabytes of data by: 🔺 𝑬𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝑺𝒄𝒂𝒏 𝑷𝒍𝒂𝒏𝒏𝒊𝒏𝒈: Iceberg enables rapid scan planning, eliminating the need for a distributed SQL engine to read tables or locate files. 🔺 𝑨𝒅𝒗𝒂𝒏𝒄𝒆𝒅 𝑭𝒊𝒍𝒕𝒆𝒓𝒊𝒏𝒈: It optimizes data reading by pruning data files using partition and column-level statistics, leveraging table metadata to filter out unnecessary data. #dataengineering #تونس_أفضل