24 months in, one thing is obvious: this was never just a fight over open table formats. It’s about who controls the metadata, the catalog, and the center of gravity for where your data gets computed. By summer 2024, three communities had spent 6–8 years building the open lakehouse. We were finally on the doorstep of real interoperability. With Apache XTable (incubating), major vendors—Google, Microsoft, and others—lined up to build open bridges across formats. Maybe for the first time ever. Except one vendor didn’t. Databricks chose to buy and merge 2 of the 3 projects—framing it as “unification,” but effectively bending Iceberg toward Delta Lake to regain control. It’s disappointing how far we still are from the promised land, even after 24 months. Through it all, we’ve stayed open, collaborative, and patient. Onehouse supports all formats equally, as we set out to do from day one. Apache Hudi supports Iceberg as a pluggable table format. The future isn’t just open formats. It’s an open lakehouse stack. We’re building it in Apache Hudi—and doubling down on interoperability through the Apache XTable project.

@apachehudi 1.2 dropped today. Multimodal data finally lands in the lakehouse. Embeddings, images, and video now sit in the same tables as your structured data—no new silos. 🚀

One foundation for structured, semi-structured, and multimodal data. If you’re building retrieval, recommendations, AI observability, or streaming pipelines, try Hudi 1.2 and help shape the next open lakehouse. Huge thanks to the entire community that made this happen. 🙏

It’s snowing in the San Francisco summer.. Most companies offload their Snowflake ETLs to spark; We launched Quanton on Snowflake. So, you can finally run real cutting-edge Apache Spark, inside Snowflake.. For the what, how, why: checkout Kyle Weller ‘s blog here 👉 onehouse.ai/blog/real-apache…

📉 In 2025, Onehouse's revenue growth was affected—not from churn or slower market, but because we rolled out Record-Level Indexing (RLI) across our biggest pipelines. RLI (used at Uber-scale with Apache Hudi) is key reason Onehouse is the most efficient option for large-scale ingestion/ETL, often cutting Spark ingest/ETL compute 50% . As customers adopted it, metered compute hours dropped ~50% even as data grew. This reveals a deep problem within the industry and also why platforms don't optimize harder with techniques like indexing. incentives! 💰 For EMR/Databricks Classic: faster jobs → lowers revenue. Not good for vendor. 🏦 For Databricks serverless/Snowflake: faster jobs → better vendor margins. No direct benefits to user. 🔄 What did we do?: volume-based pricing for Spark SQL pipelines on Quanton.dev running in the user's cloud. ⚡ We make Quanton faster → directly lowers user's compute spend → Onehouse earns based on different metric: GB processed/month. #ApacheHudi #ApacheIceberg #DeltaLake #Lakehouse #CloudOptimization #DataEngineering

Quanton matches Databricks Photon on reads and is much faster on writes—up to 4x faster/cheaper Spark at this price. DM or leave comments - for feedback!

⚡ Speed is Iceberg’s biggest Achilles’ heel. Most teams chase Velox or Gluten for Spark speed. Yet the real wins sit in fast I/O and layers untouched. We’ve spent years making lakehouse pipelines fast—but this isn’t a table-format debate. It’s slow I/O, shuffles, and reprocessing driving costs up. Speed is a stack: ⚡ SIMD 📊 Columnar processing 🔍 Query plans 💾 Fast I/O 🗄️ Metadata 📐 Storage layout 🔗 Indexes 🔄 Merging Full-stack engine: 3–4× faster than OSS Spark on Iceberg, better price/perf than Photon, no lock-in.