What Observability Can Learn From BI
summary
Argues that observability must decouple ingestion, storage, compute and UIs the way analytics did, using Kafka and OpenTelemetry for transport, S3/column formats for storage, and pluggable UIs like Grafana. Presents a storage engine (Imply/Lumi) that hooks into Splunk federated search and Grafana via a Loki-compatible API, claims 3x compression on mixed structured/unstructured data and up to 20–30x search speed for certain queries, supports SPL/JDBC/S3 pulls, and lets teams swap components without rip-and-replace.
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chapters (6)
- 0:00Introduction and background
- 3:55Architectural principles for observability
- 10:11Pain points in observability today
- 14:05Decoupling observability components
- 21:11Interoperability with existing tools
- 23:21Call to action and summary
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