Delta Lake 4.0 now supports Variant data type designed for working with semi-structured data like JSON with evolving schemas!. Key capabilities include:.🔹 𝗦𝗰𝗵𝗲𝗺𝗮-𝗼𝗻-𝗿𝗲𝗮𝗱: No need to define a fixed schema in advance. Structured data can be extracted at query time. 🔹

Missed today's “Delta Live! Live Rust hacking and AMA” 🦀 with R. Tyler Croy and Robert Pack?. Catch the full recording on our YouTube channel ➡️ 🎥 Next stream is on Tuesday, August 26 at 7AM PT!. #opensource #oss #rustlang #deltalake

Amsterdam! 🇳🇱 The Open Lakehouse Meetup is happening on Aug 27. Hear from Chang She (@changhiskhan) — Co‑founder & CEO of @lancedb and co‑author of pandas — on how to scale #multimodal AI pipelines across text, video, audio, images, and sensor data at petabyte scale. 📅

NOW LIVE! 🚨. Tune in to Delta Lake Live! with Tyler Croy & Robert Pack to do some kernel hacking together. 🦀🙌. 📽️ #opensource #oss #rustlang #kernel.

Join R. Tyler Croy, Delta Lake maintainer and founder of Buoyant Data, for “Delta Live! Live Rust hacking and AMA” 🦀 on Twitch. 📅 Tuesday, August 19.⏰ 7:00AM PT / 10:00AM ET.🎥 Whether you’re curious about delta-rs, want to see open source development

⚡️ Faster, More Reliable Table Reads with Version Checksums in #DeltaLake!. The Delta Lake Java Kernel now supports version checksums—think of them as mini-checkpoints for your Delta logs that supercharge your data operations. Here’s what you gain:.🔹 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱

🇳🇱 Join us in Amsterdam on August 27 for the Open Lakehouse Meetup!. Discover how your lakehouse is ready for production-scale multimodal ML pipelines in "Your Lakehouse Has Everything You Need" with @Sammy_Sidhu, CEO & Co-Founder of Eventual!. See how to process millions of

Exciting update in Delta Kernel Java! 🎉. Kernel now allows engines to write file statistics to the Delta log whenever available. This enhancement allows for more effective data skipping during queries by leveraging filters at read time. The result? Faster queries, optimized

RT @lancedb: Open Lakehouse Meetup in Amsterdam on Aug 27th with @lancedb and @databricks. Hear from @changhiskhan @lancedb CEO & Cofounder….

Delta Lake 4.0 brings big improvements to the internal table features framework in Delta Kernel. Now, engines using Delta Kernel can append to tables with powerful features like deletionVectors, v2Checkpoint, and timestampNtz—all enhancing reliability and performance. 👉 Explore

📢 Join us in Amsterdam for the 𝗢𝗽𝗲𝗻 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝗠𝗲𝗲𝘁𝘂𝗽!. 🔗 Register here: Session Spotlight 🌟.𝘊𝘰𝘮𝘱𝘰𝘴𝘢𝘣𝘭𝘦 𝘖𝘱𝘦𝘯 𝘛𝘢𝘣𝘭𝘦 𝘍𝘰𝘳𝘮𝘢𝘵𝘴 – 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘯𝘨 𝘖𝘱𝘦𝘯 𝘛𝘢𝘣𝘭𝘦 𝘍𝘰𝘳𝘮𝘢𝘵𝘴 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦

Hannes Mühleisen, Co-Creator of @duckdb, will be talking about the DuckLake project at the Open Lakehouse Meetup in Amsterdam on August 27th! Don't miss it. 🦆🦀. Sign up here ➡️ #duckdb #opensource #oss #deltalake #openlakehouse

Looking to go deep on the best data engineering has to offer on open lakehouse systems — with others who build and contribute to them? The Open Lakehouse Meetup is for you! 🚀. 📅 Wednesday, August 27.🕒 5:00 – 9:00PM.🇳🇱 Amsterdam, Netherlands. Register now to save your spot ➡️

Missed @YoussefMrini's talk on the latest Delta Lake latest and most impactful features at Data+AI Summit? The full session is now live on YouTube! 😎. Find out how Deletion Vectors, Liquid Clustering, and more can take your analytics to the next level. 🎥 Watch now:

“We picked Rust because it makes it easier for new contributors to get involved with high-performance development. It’s straightforward for people to start working with, and it integrates really well with Python.”. In this clip, @JayChia5 explains how @daftengine's choice of

Curious about how Delta Lake structures its tables and achieves both scalability and reliability?. In our recent webinar, Scott Haines breaks down the building blocks of a #DeltaLake table:.🔹 Partitioned storage — boosts performance and query efficiency.🔹 Parquet files —

🎥 Now Available: Delta-Kernel-RS — Unparalleled Interoperability Across Query Engines. At #DataAISummit, Robert Pack and Zach Schuermann (@Databricks) introduced Delta-Kernel-RS — a new #Rust implementation of the Delta Lake protocol designed for unparalleled interoperability

In this walkthrough, ChanChan Mao with @daftengine shows how #Daft and #DeltaLake can work together by merging complex datasets (image metadata, annotations, categories) and writing them natively to Delta Lake, complete with partitioning and Delta log tracking—no #JVM, no #Spark

In this clip, Ion Koutsouris (Rustacean & Delta Maintainer) explains how @lakeFS garbage collection policy integrates with #DeltaLake to manage unreferenced files and automate cleanups. By leveraging Spark jobs, lakeFS ensures your Delta Lake storage remains lean and organized.

“We cut our streaming ingestion costs over 90% by adopting kafka-delta-ingest, which means we can invest those savings in really interesting large language model products or innovative data processes.” — R. Tyler Croy, Principal Engineer at @Scribd & Maintainer of delta-rs 💬