Operations | Monitoring | ITSM | DevOps | Cloud

Tune in for spotlights of Bits AI SRE, now generally available, and Datadog’s MCP Server, which connects AI agents to our platform by ingesting prompts and mapping them to Datadog resources and data. Plus, we cover how to: Search logs at petabyte scale in your own infrastructure with CloudPrem Break down costs drivers at the prefix level with Storage Management Create workflows that adapt to real-world complexity with Agent Builder Detect and block credential leaks with Secret Scanning.

See how Datadog turns cloud usage and performance data into actionable business insights by helping teams calculate unit economics to measure and optimize the efficiency of every service. You’ll discover how to: Datadog bridges the gap between cloud costs and business value—helping organizations get the most value out of their cloud investment.

See how Datadog Cloud Cost Management combines observability and cost data with actionable automation to help teams optimize spend. In this short demo, you’ll learn how to: With Datadog Cloud Cost Management, cost optimization is built into the same platform engineers use every day.

Never contributed to open source and feeling intimidated? Same. Before joining Datadog, Alessandro had zero open source experience. Now he's a regular contributor to Apache Iceberg. Here's exactly how he got started. Step 1: Join the Slack community and answer user questions. Step 2: Look for "good first issue" tags in the repo. Step 3: Remember that opening bug reports and doing code reviews count as contributions too.

Learn more by watching the full episode our latest This Month in Datadog series.

Everyone talks about how great Apache Iceberg is, but nobody warns you about this: without proper maintenance, your tables will bloat, queries will slow down, and your catalog will run out of memory. Here are the 4 critical operations you MUST run regularly. Expiring snapshots prevents metadata bloat (Datadog learned this the hard way with catalog memory pressure). Deleting orphan files cleans up failed writes. Compacting data files keeps streaming workloads fast. Compacting manifests optimizes query planning.