Onehouse

Looking for some #datalakehouse goodness after the holidays? Join this webinar next month with Vinoth Chandar and Kyle Weller to learn how Onehouse can help you accelerate performance 🚀 and reduce costs (save big 💰) across Apache Hudi, #Iceberg, and #DeltaLake with ease.

You have open table formats. Now how about speeding those up? 🚀 Join Vinoth Chandar and Kyle Weller next month to learn how Table Optimizer from Onehouse can accelerate performance and reduce costs (save 💰) whether you are working with #ApacheHudi, #ApacheIceberg, and/or #DeltaLake. 2-10x faster queries, 20-80% infra cost reductions. See you there! https://lnkd.in/gKqExqUT

🎁 Early holiday gift from Onehouse: the top 5 tips for scaling Apache Spark! Tired of shuffle failures and out of memory errors? Spark jobs running too slow? We demystified the most important considerations to scaling your Spark workloads, including: - On-heap vs. off-heap memory - Spilling to disk - Optimizing your data structures - Choosing the right serialization technique - Adaptive query execution - Dynamic allocation - … and more! These tips come from the Onehouse team’s experiences operating and taming complex, petabyte-scale Spark workloads for the largest data lakes on the planet. Read the blog post here at https://lnkd.in/dVeXhn2E.

🚨Amazon #S3 data lakes have revolutionized the way businesses ingest, store, and analyze data, offering unmatched flexibility, scalability, and cost-effectiveness. This approach enables businesses to adapt quickly and derive value from their data in ways that traditional storage systems cannot. This blog post from Po Hong provides readers a comprehensive guide to Amazon Web Services (AWS) S3 data lakes and recommends the use of a lakehouse architecture, provided by an open source project such as Apache Hudi, in cases where improved manageability, queryability, or other desired characteristics make it worth the modest additional effort. 💪 https://lnkd.in/eBXA4kfe #Onehouse #DataLakehouse #ApacheHudi #RealTimeAnalytics #DataStreaming #DataEngineering 

Is your team overburdened managing #ApacheSpark? Learn how Conductor eliminated complex #Spark management, reduced query times by >75%, and freed their engineering team to focus on innovation by transforming their data infrastructure with Onehouse. #dataengineering #datalakehouse https://lnkd.in/gJBeeehW

⚒️ Are your Apache Hudi pipelines on Amazon #EMR fully optimized? 💡 Join our webinar to see how you can cut costs by up to 80% and boost performance without rewriting a single line of code. ✍️ Save your spot now!

Coming up soon! Don't miss this webinar if you are working with Apache Hudi, especially if you are building pipelines on Amazon #EMR. Accelerating data pipelines while cutting costs - get the top tips and tricks from the experts. https://lnkd.in/ePSpyybE

🚀 Just Launched: LakeView Insights + New Deployment Models! 🚀 Managing Apache Hudi tables just got a whole lot easier. Our new LakeView Insights brings the answers data engineers need, right to your inbox! 📬 Now you can: 🔍 Get quick insights on ingestion volumes, query latency, and storage layout issues ⚡ Deploy effortlessly with Pull, Push, and the new SyncTool options (demo videos in the comments) 🔒 Keep your data private – LakeView reads only your Hudi metadata, nothing else! If you’re running Hudi, LakeView is your go-to for streamlined monitoring and optimization. Start for free and experience the LakeView difference today! Sign up at https://lnkd.in/g7ZZ4dDT. Read the latest and see all the demos at https://lnkd.in/gSYmbxcy

Are you trying to choose a metastore or a catalog, but not sure what to pick? Read our latest comprehensive comparison article where Kyle Weller tears down some of the top Catalogs on the market: Unity Catalog, Apache Polaris Incubating, DataHub, Glue, Apache Gravitino, and Atlan. In this deep dive you will find head-to-head comparisons of features ranging from access controls, data quality, data discovery, and much more. The article breaks down the difference between a metastore and a business catalog, and it describes the intricate relationships of catalogs and lakehouse open table formats Apache Hudi, Delta Lake, and Apache Iceberg. Sneak peek at the rankings: Data Discovery and Exploration    ✅ Best = Atlan     ❌ Worst = Apache Polaris  Data Connectors    ✅ Best = DataHub     ❌ Worst = Apache Polaris Access Control    ✅ Best = Apache Polaris     ❌ Worst = DataHub Compliance    ✅ Best = DataHub     ❌ Worst = Apache Gravitino Data Lineage:    ✅ Best = Databricks Unity Catalog     ❌ Worst = Apache Polaris Data Quality:    ✅ Best = Glue     ❌ Worst = Unity Catalog OSS Read the blog for the full descriptions, links to docs, and the metastore rankings: https://lnkd.in/gBvA6YiK #datacatalog #unitycatalog #datahub #apachepolaris #atlan #awsglue #apachegravitino #dataengineering #apachehudi #apacheiceberg #deltalake

🌊 AWS re:Invent 2024 Recap: AI & Open Table Formats Take the Spotlight From S3 Tables to SageMaker Studio, AWS delivered major announcements at this year’s #reInvent, reinforcing 2024 as the year of AI and Open Table Formats. Key highlights for data teams: ✅ S3 Tables – Native integration with open table formats with the hope of easier, faster queries and automated table maintenance. ✅ Glue 5.0 – Data lineage, fine-grained access control, and version upgrades for  Apache Hudi, Apache Iceberg, and Delta Lake. ✅ SageMaker Lakehouse + Studio – A unified interface for analytics and AI, centralizing tools like Redshift, Glue, and SageMaker. We break down the benefits, drawbacks, and what these updates mean for your data lakehouse strategy. 👉 Dive deeper into our full blog: https://lnkd.in/ensbpTwt #AWS #DataLakehouse #OpenTableFormats #AI