Onehouse
@Onehousehq
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Onehouse is the universal data lakehouse, offering a cloud-native managed lakehouse built on @apachehudi, accessible across table formats, engines and clouds.
Sunnyvale, CA
Joined September 2021
🎉 Exciting News! For Onehouse and those rooting for the open data lakehouse 🎉 We are happy to announce our $35M Series B round of funding, led by @craft_ventures. The new funding adds more fuel to the Onehouse rocketship, accelerating how we redefine the cutting-edge of
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⏰ We’re going live in just a few minutes! The Open Source Data Summit 2025 kicks off shortly. There's still time to grab your free virtual spot! Hear open data stack best practices from Uber, Walmart, ADP, Apache Software Foundation, Intercom, Onehouse, PayPal, and more.
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Quanton accelerated Iceberg workloads now at 3x performance vs OSS Spark and up to 5x price/performance vs other premium Spark engines
🚀 Quanton now also powers Apache Iceberg natively — delivering 3× faster Spark workloads! When we launched Quanton, the goal was ambitious: make Spark truly lakehouse-optimized — faster, smarter, and format-aware. 👇
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📣 We're excited to be sponsoring Open Source Data Summit, coming up this Thursday 11/13. 🗓️ Nov 13, 2025 | Free virtual event 👉 https://t.co/wCCXdOoiWU Join thousands of practitioners and hear from Data and AI experts from Uber, Walmart, ADP, Apache Software Foundation,
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Honored to share that Onehouse has been named to @CRN's 2025 Stellar Startups list in Big Data 🎉 We’re excited to see our approach recognized: → simplifying lakehouse operations → keeping data open and interoperable → cutting Spark costs by 50%+ Congrats to all the
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Your lakehouse might be fast… but is it secure? 👀 As lakehouses scale, small security gaps become big risks: encryption, access control, compliance. We just published a deep dive on securing data lakehouses: 🔐 End-to-end encryption (data at rest + in transit) 👥 Role- and
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💸 Most teams running Apache Spark™ are burning 30-70% of their compute budget, and they don’t even know it. Why? Because Spark’s defaults are built for throughput, not efficiency. On Nov 18, join us for a live session on The True Cost of Spark, and how to cut it in half.
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📘 The full “@apachehudi: The Definitive Guide” is out (free). Why it matters: Lakehouses are mainstream, but reliable, incremental, cost‑efficient pipelines are still hard. This guide distills battle‑tested patterns from real‑world Hudi deployments. What’s inside: ingestion +
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We are live at Current in New Orleans 🔥 Come by booth 305 for a quick demo, great convo, and some swag!
📍 Next stop: Current 2025 in New Orleans! Stop by booth 305 to see how Onehouse can help: ⚡ Ingest from Kafka with minute-level freshness at scale 💸 Slash ETL costs by 50%+ Plus, we’ve got some top-tier swag waiting for you 😎 #Current25 #ApacheKafka #DataEngineering
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Are your Apache Druid, Pinot, or ClickHouse queries crawling 🐢? In our latest blog post, we unpack query tuning, partitioning, and caching: the real levers that separate “just works” from “production-grade fast” in open source data warehouses. You’ll learn: ⚙️ How query
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📍 Next stop: Current 2025 in New Orleans! Stop by booth 305 to see how Onehouse can help: ⚡ Ingest from Kafka with minute-level freshness at scale 💸 Slash ETL costs by 50%+ Plus, we’ve got some top-tier swag waiting for you 😎 #Current25 #ApacheKafka #DataEngineering
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Think your Spark jobs are tuned? Think again. 🧩 We’ve analyzed hundreds of real-world Spark workloads across EMR, Databricks, OSS. Most waste 30-70% compute. On Oct 23 @ 10am PT, we’ll show how to: 🧠 Identify stage-level waste hiding in your DAGs ⚙️ Fix autoscaling blind
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We’re excited to join the dbt community at #dbtCoalesce next week! Come by booth #426 to see how Onehouse + dbt make ETL pipelines 50% cheaper and 2-3x faster, with zero code changes. Also… you’re gonna love our swag. 😎 See you there! #dbt #DataEngineering #DataLakehouse
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Still on the fence about a #Lakehouse? Your competitors aren’t. ⚠️ According to Radiant Advisors’ latest survey: 💡 AI and GenAI use cases now drive half of all data architecture investments (49.4%) 📅 82.6% of enterprises are implementing by year-end 🏗 The Lakehouse ranks as
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Most #Spark jobs waste 30-70% of compute 💸 Slow autoscaling, skewed joins, inefficient shuffles… they add up fast. Do you actually know what’s happening inside your Spark jobs? Join us for this live deep dive where we’ll cover: 🔹 How to identify stage-level bottlenecks
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Tired of your data ingestion pipelines feeling like a bad blind date? Promising real-time magic but delivering batch-mode disappointment? Dive into the deep comparison: Kafka Connect vs. Flink vs. Spark for slurping data into your lakehouse. https://t.co/uVnB1w67y7
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Are table format wars ⚔️ over yet? Yes, but not in the way you expect. Delta Lake is in the rearview mirror. Apache Hudi has also moved on from format wars as it now has native Iceberg "format" support. Freshly updated for 2025, but this time, with a fresh new lens. LINK 👇
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An uncomfy truth... ⚠️ Most Spark jobs waste compute + eng time We consistently see 30-70% waste 🧟. Not from lazy engineering, it's a challenge that stems from Spark's complexity. Our Spark Analyzer ⚡ PyPI package helps turn logs into insights 👉 https://t.co/gG3YJ417va
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New work on Generic Table APIs from Snowflake and Onehouse in Polaris uses XTable for non-iceberg tables. Running XTable via REST API = POST /v1/conversion/table/ {"source-format": "HUDI | DELTA" ...} Decoupling catalog spec from formats future proofs innovations in storage
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Running Spark today? Try our free Spark Analyzer and see how much inefficiency you’re paying for. 👉 https://t.co/I8ihhcUCls Read the blog: 👉 https://t.co/13g7d6z0R1
#ApacheSpark #Lakehouse #ETL #DataEngineering #Onehouse
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