Vinoth Chandar @byte_array

Founder @Onehousehq, Creator of @apachehudi, Built the World's first #DataLakehouse, Distributed/Data Systems, Linkedin, Uber, Confluent alum. (views are mine) bytearray.io Joined April 2009

Tweets

1K
Followers

2K
Following

232
Likes

757

Apache Hudi @apachehudi

21 hours ago

GE Aviation: 30+ source systems in production, several hundred Apache Hudi tables, 14+ months in production, 10,000+ tables in the dev pipeline. Modernizing 150+ source systems across the aviation ecosystem ↓

1 2 5 137 1

View Details

Vinoth Chandar @byte_array

2 days ago

If you read receipts, a new pricing page with a calculator to break this all down: quanton.dev/pricing/

0 0 1 121 0

View Details

Vinoth Chandar @byte_array

2 days ago

My biggest learning yesterday at the Data + AI Summit: Most users said they "turn off Photon by default," or "run careful benchmarks" to decide if the ~2.9x Photon tax is worth it. Kind of wild that it's still the way things are. standard clusters (slow, tax-free) (OR) photon clusters (fast, taxed). This is no way to be running Apache Spark in 2026. We're still chatting at booth 403 — I'm here all day, entertaining questions and also healthy debates. Swing by! #ApacheSpark #Performance #DataAISummit #Databricks #Photon #Quanton

1 1 8 633 0

View Details

Vinoth Chandar @byte_array

3 days ago

Our Data + AI Summit booth number is 403. Yes, the HTTP error code for "Forbidden". 🏴‍☠️🦜 But you've seen the movies. The treasure is always in the forbidden place. The sealed cave. The remote island. The door someone told you to stay away from. That's where they keep the good stuff. Booth 403 is that door. Behind it: Quanton, our new Spark engine — ~3x the Spark for the same spend. If you're paying a fortune and still watching progress bars crawl, this is the chest you've been digging for. Come plunder. See what else is out there. And throw your name in for some Nintendo Switch 2s while you're at it. 🎮 X marks the spot. Booth 403. #DataAISummit #apachespark #databricks

0 3 7 237 0

View Details

Vinoth Chandar @byte_array

a week ago

Blog : onehouse.ai/blog/databrick…

0 0 3 118 0

View Details

Vinoth Chandar @byte_array

a week ago

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.

1 2 10 408 2

View Details

Vinoth Chandar @byte_array

2 weeks ago

Full release blog: hudi.apache.org/blog/2026/06/0… Star us on GitHub: github.com/apache/hudi

0 0 0 26 0

View Details

Vinoth Chandar @byte_array

2 weeks ago

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. 🙏

1 0 0 45 0

View Details

Vinoth Chandar @byte_array

2 weeks ago

@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. 🚀

1 0 7 358 0

View Details

Vinoth Chandar @byte_array

2 weeks ago

@Amit_official97 Yes. Everything stays within snowflake ecosystem if thats what you prefer.

0 0 1 13 0

View Details

Vinoth Chandar @byte_array

2 weeks ago

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-apac…

1 1 5 1K 1

View Details

Vinoth Chandar @byte_array

2 weeks ago

Onehouse isn’t at Snowflake Summit this year (let’s not go into reasons). But someone should be having the real Spark-on-Snowflake conversation — so we showed up anyway. On a truck 🚛 Here’s why: - Real Apache Spark — inside Snowflake containers - Iceberg writes up to 4× faster - No lift-and-shift, no second platform — especially if your data already lives in parquet/iceberg/delta/hudi - Sitting on unspent Snowflake credits? Use them for real Spark instead of paying yet another vendor - And since Snowpark containers can be cheaper than Snowflake warehouses, you could spend up to 65% fewer credits We didn’t build Quanton to win a benchmark slide. We built it so data teams don’t have to move their data, double their bill, or pick a side in someone else’s platform war just to run a workload. At Summit? Wave at the truck. Not here? quanton.dev/docs/guides/sn….

0 5 18 122K 2

View Details

Vinoth Chandar @byte_array

3 weeks ago

More honesty is good. 👍🏻 Its very frustrating when AI (also) lies about work.

Claude @claudeai

3 weeks ago

Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper judgment, more honesty about its own progress, and the ability to work independently for longer than its predecessors. Available today at the same price.

4K 9K 67K 15.3M 8K

0 0 1 170 0

View Details

Apache Hudi @apachehudi

3 weeks ago

Picture your lakehouse bogged down by updates at 10TB scale 📊? Core issue: Finding records without scanning full table. Hudi's file groups route same-key records to one group with base/log files 🔑. Updates are bound to that group 🚀—skip full searches. Wins: • Bounded write locality • Limited scans for merge reads/compaction At scale, this stabilizes ingestion vs. messy maintenance or sluggish MERGE INTO 🛠️.

1 2 7 650 5

View Details

Apache Hudi @apachehudi

3 weeks ago

Building indexes on a 100 TB Hudi table without blocking writes. The naive approach: stop writes, build the index, resume. Hours of downtime. Hudi async indexing: bootstrap index, then converge , high concurrency ↓

1 2 7 762 4

View Details

Vinoth Chandar @byte_array

4 weeks ago

Check out here: quanton.dev ;

0 0 0 95 0

View Details

Vinoth Chandar @byte_array

4 weeks ago

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!

1 0 0 115 0

View Details

Vinoth Chandar @byte_array

4 weeks ago

📉 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