Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability

The podcast explores applying data lakehouse architectures to observability for improved scalability and economics. Jacob Leverich, co-founder and CTO of Observe Inc., details Observe's observability solution built on a lakehouse architecture. He shares his experience at Splunk and Google, which led to founding Observe. Leverich emphasizes that a generic lakehouse setup will fail for observability due to latency requirements. He highlights the importance of OpenTelemetry for data collection, Kafka for buffering, and a dynamic loader for balancing latency and efficiency. The discussion covers data curation, enrichment, and the abstraction of SQL to optimize query execution. The conversation also addresses the role of table formats like Iceberg and AI-native workflows in enhancing observability data management.