Apache Iceberg Delivers in Production: Improved Performance and Cost Outcomes

Apache Iceberg unlocks open, interoperable lakehouses, but running it in production is where most teams hit friction. This post breaks down how Fivetran delivers Iceberg benefits without separately running catalogs, compaction jobs, or operational overhead. A quick read for anyone building on modern data lakes. Learn more: https://lnkd.in/gz_EY8Jn

Apache Iceberg unlocks open, interoperable lakehouses, but running it in production is where most teams hit friction. This post breaks down how Fivetran delivers Iceberg benefits without separately running catalogs, compaction jobs, or operational overhead. A quick read for anyone building on modern data lakes. Learn more: https://lnkd.in/gXnvEVaE

Different query engines can see different versions of the same metadata. Without a consistent approach to managing it, this often leads to mismatched results and confusion across teams. In our latest blog, we look at how Apache Iceberg REST Catalog helps provide a consistent view of metadata across engines, and how to set it up in practice. If you’re working with Iceberg or evaluating catalog options, this is a practical guide by Ognjen Lubarda: 🔗 https://lnkd.in/dKgVBqSG

Part III: Why Apache Iceberg feels like a shift, not just a tool The more I explore Iceberg, the more I realize: It’s not solving a new problem. It’s solving an old problem properly. For years, we’ve been dealing with: • Fragile partitions • Expensive full-table scans • Painful backfills • “Hope this doesn’t break production” deployments What Iceberg does differently is simple: 👉 It moves the intelligence to the metadata layer That unlocks: 🔹 Smarter query planning (read less, faster) 🔹 Safer updates with snapshots 🔹 Decoupling compute from storage (finally done right) It’s one of those things that makes you think: “Why didn’t we build data lakes like this from the beginning?” Still learning — but this feels like a foundational piece for modern data platforms. For those using Iceberg in production — what was the biggest “aha moment” for you? BTW here is the first video I watched that got me into this journey. It is a good place to start, if you are interested to embark in it yourself https://lnkd.in/eMyv8Nu4

Q: In Apache Jackrabbit Oak, what causes a query to traverse nodes instead of using an index? A. Missing index definition B. Wrong node type C. Large dataset D. Async indexing

I’m in San Francisco this week for the Apache Iceberg Summit. Apache Iceberg has helped the industry rally around a real open standard at the storage layer. At OLake™ by Datazip, we think the same thing needs to happen one layer above it — in how data moves into and out of Iceberg. OLake is built to make ingestion into Iceberg simpler, faster, and more open, without all the brittle plumbing and lock-in that usually slow teams down. The future of open data infrastructure is not just about where data lives. It is also about how easily it gets there. If you care about fast, reliable, open data movement into Iceberg, say hello to us at the booth!! #olake #apache_iceberg #iceberg #ingestion

Spring for Apache Pulsar 1.2.15 and 2.0.3 are now available | On behalf of the team and everyone who has contributed, I’m happy to announce that Spring for Apache Pulsar 1.2.15 and 2.0.3 have been released and are now available from Maven Central. – Chris Bono https://dy.si/BJiE1S2

I've performed a thorough RCA of the TeamPCP (Trivy/Checkmarx/LiteLLM) compromise. I am actively building some tools to help, and will be releasing them to the community Apache 2.0. Hopefully Teams will hear about this, and begin to use these tools, to help deal with the situation. I think it was made clear that this will not stop. The first tool deals with understanding and awareness of the blast radius when credentials are harvested from a pipeline. It is not really easy to understand what credentials are associated with a pipeline, and what is the risk of exposure. One of the contributing factors to the full compromise chain, was that victims of the attack did not know what credentials to rotate. It is likely that "all" credentials were meant to be rotated, but also likely these are not tracked anywhere (no baseline), so there is a risk that you miss something (this is what happened with Trivy). So this first tool will be a triage tool, you will load your pipeline workflow files (locally of course), and the tool will map which credentials are reachable from each job, and produce a prioritized rotation list. This will give organizations an ability to react to the compromise, become aware of the exposure or blast radius, and begin rotating credentials based on risk. This should bring the rotation time down from days to minutes.

A practical perspective on how modern data systems actually perform is coming to Iceberg Summit 2026, hosted by Apache Iceberg. Developer Evangelist Brenna Buuck brings a hands-on look at where things break—from object storage’s impact on Iceberg query times to building real, working agents. A clear view into where performance is won (or lost). See you in San Francisco. 🌁 https://lnkd.in/gdf32zj2

Apache SeaTunnel March update is out 🚀 5 new connectors, 50+ features, and major CDC improvements. Strong momentum from 26 contributors. Time to upgrade and explore what’s new. #ApacheSeaTunnel #DataEngineering #BigData https://lnkd.in/gemyv_aN