EDB’s Post

Level up your Postgres expertise with EDB’s January 2025 free virtual workshops 👇 These five sessions are crafted for technical professionals who want to master PostgreSQL, explore AI-powered Postgres solutions, and optimize database management at scale. Each session is built around real-world use cases and practical insights you can immediately apply to your work. Workshop schedule: 🔸 Designing and Managing Databases in EDB Postgres Master database design, access control, and performance tuning. 🗓️ Jan 8, 12:00 pm IST / 10:00 am ET 🔸 Introducing the EDB Postgres AI Platform Learn how AI-powered Postgres can enhance data management and performance. 🗓️ Jan 9, 12:00 pm IST / 10:00 am ET 🔸 EDB Postgres System Architecture Dive deep into the architecture—storage, memory, transaction processing, and more. 🗓️ Jan 15, 12:00 pm IST / 10:00 am ET 🔸 Postgres Data Estate Management using EDB Postgres AI Learn AI-driven strategies to optimize your Postgres data estate. 🗓️ Jan 22, 12:00 pm IST 🗓️ Jan 30, 10:00 am ET 🔸 Getting to Know the EDB Postgres AI Cloud Service Explore how EDB’s AI-powered cloud service simplifies Postgres deployment and management. 🗓️ Jan 29, 12:00 pm IST / 10:00 am ET Register here 👉 https://bit.ly/3TOO19v #EDBPostgresAI #FreeWorkshop #PostgreSQL #DataEstateManagement #CloudDatabase #DatabaseMigration #DBA #FutureOfAI #JustSolveItWithPostgres

A really good new propeldata.com engineering blog post just landed → https://lnkd.in/gWxZnUD4 If your running MongoDB and need to power analytics, it covers how to: ◆ Set up MongoDB Change Data Capture with Debezium ◆ Stream the changes to Kafka ◆ Ingest from Kafka to Propel ◆ Build analytics with an API Includes step-by-step guide + code examples 🤩

🚀 Unlocking Data Efficiency with Apache Hudi 🚀 In the fast-paced world of big data, efficient data management is a game-changer. Apache Hudi is a powerful tool that enables near real-time data ingestion and provides a robust framework for managing large datasets effectively. 🔍 Why Apache Hudi?  ✨ Incremental Processing: Eliminates the need for full dataset rewrites by allowing incremental data processing. 📜 Data Versioning: Supports data versioning, making it easier to track changes and maintain data integrity. 💾 Efficient Storage: Significantly reduces storage costs and enhances query performance. 💡 Key Benefits: ⏱️ Real-Time Data Ingestion: Seamlessly ingest and process data in near real-time. 💰 Cost Savings: Optimize storage and compute costs with efficient data management. 🔧 Flexibility: Integrate with various data processing frameworks like Apache Spark, Flink, and Hive. In a recent project, I offloaded millions of rows from multiple sources, including relational databases and streaming platforms, into S3 as Parquet files and then built Hudi tables. By leveraging the AWS Glue Catalog, we were able to efficiently manage and query our metadata. The result? A dramatic reduction in storage size and improved data processing efficiency. 🔍 Additional Insights: Scalability: Hudi scales seamlessly with growing data volumes, ensuring optimal performance even as datasets expand. Data Quality: Ensures high data quality with built-in support for data cleaning and deduplication. Compliance: Facilitates compliance with data governance and regulatory requirements through detailed audit trails and data lineage tracking. Community and Support: Benefit from a vibrant open-source community and extensive documentation, making it easier to implement and troubleshoot. 🌟 Excited to see how Apache Hudi continues to evolve and transform data management practices! 🌟 #BigData #ApacheHudi #DataManagement #RealTimeData #DataEfficiency #CostSavings #Innovation #TechTrends #Scalability #DataQuality #Compliance #AWSGlue

💡 Just completed a Big Data Training at BT Akademi and earned my certificate! Gained practical insights into MongoDB, CI/CD processes, and big data workflows. Excited to apply these skills to innovative, data-driven projects. #BigData #MongoDB #CICD #ContinuousLearning

Here are the 10 most asked interview questions on Apache Hudi 👇 ✅ What is Apache Hudi, and how does it work in a data lake architecture? ✅ What are the different table types supported by Apache Hudi, and how do they differ? ✅ How does Apache Hudi handle upserts, and why is it important for data lakes? ✅ Can you explain the Merge-on-Read (MOR) and Copy-on-Write (COW) table types in Hudi and when to use each? ✅ What is the role of Apache Hudi’s timeline service, and how does it track changes? ✅ How does Hudi achieve incremental data processing and how can it benefit ETL workflows? ✅ How do you configure and optimize Apache Hudi for partitioned data in large datasets? ✅ What are Hudi’s compaction strategies, and why are they important for Merge-on-Read tables? ✅ How does Apache Hudi integrate with popular processing engines like Apache Spark and Presto? ✅ What challenges have you encountered in implementing Hudi in a production environment, and how did you resolve them? 🚨 Join my high quality, industry & modern tech stack driven (AWS, GCP, Snowflake, Databricks, Flink, Iceberg, Hudi and so on) and practical project driven Data Engineering 4.0 With AWS 👇 👉 Enroll Here - https://lnkd.in/d4SS8JTm 🔗 Code "FIRST50", valid for only first 50 users 🚀 Live Classes Starting on 9-Nov-2024 📲 Call/WhatsApp for any query (+91) 9893181542 Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Shubhankit Sirvaiya Sahil Choudhary Aman Kumar Rahul Shukla

Our 2024 Trend Report, "Database Systems: Modernization for Data-Driven Architectures" is live! 📈 Download it now: https://lnkd.in/dNsZaHcQ In 2024, the focus around databases is on their ability to scale and perform in modern data architectures. It's not just centered on distributed, cloud-centric data environments anymore, but rather on databases built and managed in a way that allows them to be used optimally in advanced applications. Read our expert articles to learn more: 👋 "Welcome Letter" by Shantanu Kumar 🐘 "Just Use PostgreSQL: A Quick-Start Guide: Exploring Essential and Extended Capabilities of the Most Beloved Database" by Denis Magda ⏳ "Leveraging Time Series Databases for Cutting-Edge Analytics: Specialized Software for Providing Timely Insights at Scale" by Ted Gooch 🪶 "Real-Time Streaming Architectures: A Technical Deep Dive Into Kafka, Flink, and Pinot" by Abhishek Gupta ♾️ "Automating Databases for Modern DevOps Practices: A Guide to Common Patterns and Anti-Patterns for Database Automation Techniques" by Naga Santhosh Reddy Vootukuri 🤖 "Pivoting Database Systems Practices to AI: Create Efficient Development and Maintenance Practices With Generative AI" by Anandaganesh Balakrishnan This report would not be possible without the support of our sponsoring partners, StarTree and pgEdge! #database #postgreSQL #apache #AI

Stack Overflow Developer's survey yesterday and DZone Database trends report today. Both reports confirm that Postgres has remained the most popular database. As a bonus, the DZone report comes with the "Just Use Postgres" quick-start guide for those developers who have yet to explore the breadth and depth of Postgres capabilities. This is my way of contributing to Postgres; we need even more developers to give the database a try.

The way data capabilities and databases are built, managed, and scaled has evolved at an exponential rate. It is quite remarkable to see how much databases have changed over the years into ones that allow for developers and organizations to be more flexible with their data. We touch upon this database evolution and more in our newly launched "Database Systems" Trend Report! Be sure to check it out at the link below! #databases #datamodernization #postgresql

Checkout our Annual Database Systems Trend Report that we just published today...always one of the most anticipated of the year with our DZone audience! As always, thanks to our sponsors StarTree pgEdge contributors and publishing team! #database #AI #DevOps #DatabaseMonitoring

Table Services in Lakehouses. Lakehouse platforms like Apache Hudi enables users to leverage the benefits of open data architecture & faster updates and deletes with ACID guarantees on cloud data lakes such as AWS S3. However, there is also the need to maintain a lakehouse platform so it can deal with things such as: ❌ small-file problem ❌ data co-locality ❌ merging data & log files for Merge-on-Read tables ❌ reclaiming space occupied by older data versions, etc. Hudi's platform is equipped with a variety of native table services designed to optimize table storage configurations & metadata handling. Hudi was designed with built-in table services that enables running them in inline, semi-asynchronous or full-asynchronous modes. Here are some of the key services: ✅ Compaction - implements strategies such as date partitioning and I/O bounding, merging Base Files with Delta logs to create updated Base Files. ✅ Clustering - allows the grouping of frequently queried records by using sort keys or merging smaller Base Files into larger ones, enhancing file size management. ✅ Cleaning - Cleaner service works off the timeline incrementally, removing File Slices that are past the configured retention period for incremental queries, while also allowing sufficient time for long running batch jobs to finish running. ✅ Indexing - Through asynchronous metadata indexing, Hudi enables the creation of various indices without impacting write operations. This method not only improves write latency but also minimizes resource waste by minimizing conflicts between writing and indexing tasks. Read more about the Hudi's lakehouse platform in comments. #dataengineering #softwareengineering