Apache Iceberg is a mature open table format that’s been battle-tested in the broader analytics world for years. Now, it’s time to apply its benefits to an observability field that desperately needs to break out of its siloed heritage.

While OpenTelemetry has made significant strides in collecting metrics, logs, and traces, most observability stacks still fragment this data into isolated silos. Joining business data with observability typically involves costly and error-prone processes like exporting, duplicating, or downsampling. This makes simple questions like “Which customers were affected by an outage?” or “What was the revenue impact?” into bespoke data projects.

The Power of Open Standards

Apache Iceberg standardizes how large analytical data sets are stored and evolved on object storage, offering features like ACID transactions, snapshot isolation, time travel, and schema evolution. It’s a neutral table layer that any compatible compute engine can use, including Spark, Flink, Trino/Presto, Dremio, and major cloud data platforms.

This allows telemetry to be treated as first-class data that lives alongside customer, finance, and product tables without endless copy pipelines. With Iceberg, you can explore telemetry with a SQL engine, a notebook, or your existing BI tools without transferring terabytes of data.

Iceberg Advantages for Observability

The breakthrough Iceberg achieves for observability is that it lets you keep logs, metrics, and traces as data sets in the same lakehouse that already holds business data. This enables seamless exploration and analysis of telemetry data with existing tools and infrastructure.

Iceberg’s schema evolution feature is particularly well-suited for observability’s frequent schema changes. Adding a new label, renaming a field, or adding a late-arriving dimension from an upstream service is no longer a big deal. Iceberg’s hidden partitioning features also let you add or rename columns and adjust partition specifications over time without rewriting historical data or breaking queries.

Iceberg’s manifest and snapshot model provides atomic commits, compaction, and data skipping so engines can sustain write pressure while keeping read latencies predictable. This is critical for high-volume telemetry pipelines, which need consistent, append-heavy writes with occasional deletes and backfills.

Time Travel and Revisions

Iceberg’s “time travel” feature lets you query a table exactly as it looked at some point in the past. This is ideally suited for queries like “What changed between 09:00 and 09:05?” Snapshot metadata also lets you query the table as it existed at a prior point in time, compare revisions, and reconstruct the state without requiring parallel stores or ad-hoc archives.

Iceberg’s open format makes it an attractive solution for observability use cases. By breaking down silos and treating telemetry as first-class data, Iceberg can revolutionize the way we approach observability and analytics.

Inspired by

• https://www.infoworld.com/article/4066477/why-observability-needs-apache-iceberg.html

Sources

• apache.org
• opentelemetry.io
• cio.com