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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.

Want to make your logs easier to work with? Excluding unneeded logs from indexing reduces noise and may reduce log management costs. In this video, you’ll learn how to: See for yourself how to improve log utilization with Datadog Log Patterns and log exclusion filters. Then set up an alert to track ingestion spikes.

Training an AI foundation model on over a trillion data points sounds impossible without hitting your production systems. Here's how Datadog did it with Apache Iceberg for their time series forecasting model TOTO. The key challenge: extracting massive historical observability data (metrics spanning years) and running incremental preprocessing pipelines without overwhelming production services. Iceberg solved this by providing schema governance, consistency guarantees, and seamless integration with ML tools like Ray and PyTorch.

Tired of waiting days to see your AWS bill spike? Datadog solved this problem using Apache Iceberg to deliver real-time cloud cost visibility - updating every 15 minutes instead of waiting for billing data. Here's how it works: They sync real-time resource inventory (EC2 instances, Kubernetes pods) into Iceberg tables, then use Trino to join those snapshots with unit pricing data. The result? FinOps teams can catch cost anomalies before they become budget disasters.