Apache Iceberg has effectively won the open table format conversation. AWS, Google Cloud, Microsoft, Snowflake, Databricks — every major platform has thrown its weight behind it. If you work in data engineering or platform operations, the question is no longer whether Iceberg is the right foundation. It’s what it actually takes to run it day to day. That second question doesn’t get nearly enough airtime. And it’s the one that determines whether your Iceberg adoption goes well or becomes a slow-motion infrastructure project that nobody budgeted for. The Gap Nobody Talks About Here’s what Iceberg gives you: a table format with schema evolution, time travel, partition evolution, and engine independence. Here’s what Iceberg does not give you: a way to get data into those tables, a way to model and transform it once it’s there, a way to coordinate when things run, or a way to keep table health in check as data piles up. Put differently, Iceberg defines how tables behave, not how to op...
Lightrun has added an ability to dynamically pull missing telemetry evidence from live application environments without having to deploy additional instrumentation to its namesake site reliability engineering (SRE) platform that is based on artificial intelligence (AI). Company CEO Ilan Peleg said the Lightrun AI SRE platform includes a sandbox deployed via a software development kit (SDK) that can now be integrated with a live application environment to collect new evidence, test hypotheses, and validate outcomes against real execution behavior without having to deploy additional agents to collect telemetry data. The overall goal is to provide DevOps teams with much-needed additional context on demand to reduce mean time to detection of the root cause of an incident, he added. That capability will soon prove to be crucial as the volume of applications that are being deployed in the age of AI begins to overwhelm the ability of DevOps teams to manage incidents, noted Peleg. At th...