Ryft

This is a big moment: Ryft has been acquired by Cyera! 🚀 We set out on this journey a year and a half ago, with the vision of managing the world’s data in the age of AI. Joining Cyera allows us to take that vision a huge step forward, and help the largest and most cutting edge enterprises in the world adopt AI and agents safely. We’re incredibly excited for what’s ahead! 🎉

Gartner predicts 40% of enterprise apps will have task-specific AI agents by the end of 2026. This may expose performance bottlenecks that were easier to live with in human-driven workflows. Agent workloads make repeated retrieval and metadata access much more frequent. In this kind of environment, small delays in table access can turn into bigger performance issues. For teams running Iceberg, it is worth taking a closer look at metadata efficiency, table maintenance, and whether tables are actually optimized for the way they are being queried.

AI agents can query your data, but they don’t understand it 🧊 Schema can show an agent which tables and columns exist, but it doesn’t provide the context needed to judge whether a result is trustworthy – this is why agent output can look plausible and still be wrong. The Ryft Context Layer gives agents structured, agent-readable context for every table in your Iceberg lakehouse. It builds on the signals Ryft already collects across the lakehouse and turns them into context your agents can use, with room for your team to add business definitions where automation cannot infer them. See how to give your agents more reliable context at the table level → https://lnkd.in/d8SAQfgx

That’s a wrap on Apache Iceberg Summit 2026! Thanks to everyone who visited our booth, joined the conversations, and talked with us about the realities of data at scale in the agentic era. We’re excited to continue building around the operational needs for agents.

Scenes from Day 1 of Apache Iceberg Summit in SF! Yuval Yogev discusses how agents change the way we interact with our data.

Today we’re launching the Ryft Context Layer 🧊 AI agents need more than schema to work with data correctly. Schema metadata can show an agent what exists in the lakehouse, but it still leaves out the context needed to interpret the data correctly. This is why agent output can look plausible and still be wrong. The Ryft Context Layer gives agents structured, agent-readable context for every table in your Iceberg lakehouse, generated automatically from actual usage patterns. See how to make your lakehouse easier for agents to work with reliably → https://lnkd.in/d8SAQfgx

This week at Iceberg Summit 2026 in San Francisco, Yuval Yogev will be sharing how teams can approach intelligent snapshot management in Apache Iceberg, which is one of the most important operational tradeoffs in Iceberg. It’s a topic that shaped a lot of our recent thinking on Apache Iceberg backups, and one that comes up quickly once teams start weighing retention needs against recovery requirements and storage cost. If you’re running Iceberg, or evaluating it, this session should give you a practical framework for thinking through these decisions. Register here: https://lnkd.in/dBy-3sta Register here: https://lnkd.in/dBy-3sta Promo code Penguin gives 20% off

OpenAI's internal data agent failed when it relied on table schemas alone. The data and structure were there, but the agent couldn't reliably answer questions because it didn't understand what the data actually meant. The real challenge is building the right context, and as OpenAI shared in a detailed write-up, they ended up building six layers of context on top: 1. Table usage patterns from historical queries 2. Human annotations with business definitions 3. AI-powered code enrichment to understand how pipelines produce the data 4. Institutional knowledge from Slack and Docs 5. A memory system that learns from corrections 6. Live runtime queries against the warehouse 7. Only after all six layers were in place did the agent start delivering reliable results across 3,500 users and 70,000 datasets Only after all six layers were in place did the agent start delivering reliable results across 3,500 users and 70,000 datasets. https://lnkd.in/gfZ4GUnd

In our webinar with Firebolt, we showed queries going from 8.2 seconds to 0.4 seconds – 20x faster. But with streaming workloads, your data keeps changing: new files every minute, partitions growing unevenly. The goal is to operationalize your lakehouse so it continuously returns there. Yuval Yogev and John Kennedy discuss this here 👇

Apache Iceberg is delivering in production. From our 2026 State of Apache Iceberg research of 252 US data leaders running Iceberg in production: ↳ 99% report improved query performance ↳ 98% are satisfied with cost outcomes ↳ 93% say Iceberg unlocked new use cases ↳ 69% say it helped solve data consistency issues The data shows that Iceberg is delivering on its core promises, with more attention now going to the operational work that comes with broader production use. Benchmark your Iceberg operations against peers → https://lnkd.in/dV9i6Qfs