Many companies keep their telemetry data—logs, metrics, and traces—separate from their business data. This siloed approach makes it hard to ask big questions like how an outage affected customers or how revenue was impacted. Now, a new way to store and manage telemetry is changing that. Apache Iceberg offers a standard, scalable way to make logs and metrics as easy to access and analyze as business data.

Bringing Telemetry into the Same Data Lake as Business Data

Iceberg is an open table format that’s been proven in analytics for years. It allows companies to store logs, metrics, and traces right alongside customer, finance, and product data. This means teams can explore telemetry with familiar tools like SQL, notebooks, or BI platforms without copying huge amounts of data. This integration cuts down on errors and makes analysis faster and more reliable.

One of Iceberg’s strengths is how well it handles schema changes. Telemetry data often changes quickly—new labels are added, fields are renamed, or dimensions arrive late from upstream systems. Iceberg’s schema evolution feature makes these updates seamless, without rewriting historical data or breaking existing queries. Its hidden partitioning also allows for flexible data management, keeping high-cardinality telemetry manageable over time.

Enabling Reliable, High-Volume Telemetry Workflows

Iceberg’s snapshot and manifest models support atomic commits, data compaction, and skipping unnecessary data scans. This is crucial when dealing with high-volume telemetry streams. It enables companies to write data continuously, correct errors, enforce retention policies, or backfill missing info without disrupting the entire table. The “time travel” feature lets users query the state of logs and metrics at any past point, making it easy to analyze changes or troubleshoot issues by comparing different snapshots.

Security and governance are also key. Because Iceberg is an open format supported across many platforms, teams can set consistent access controls and policies across all their data. This means telemetry data can benefit from the same security measures as other critical business information, simplifying management and compliance.

Connecting Observability with Business Outcomes

Traditionally, observability has been mainly about understanding failures after they happen. But now, leaders want to see how issues impact users and business results. Iceberg makes it possible to join telemetry data directly with customer, billing, or product data. For example, teams can instantly see which customers experienced slowdowns or outages, without exporting data into separate systems. They can analyze how latency spikes affect conversion rates or cart abandonment, all within the same platform. This integration opens the door to more proactive, data-driven decision making.

Furthermore, Iceberg enhances OpenTelemetry (OTel) by standardizing how telemetry data is stored and evolved. While OTel handles data collection, Iceberg ensures that this data remains durable, queryable, and easy to manage over time. You don’t need to overhaul your existing tools—just start by moving your most valuable telemetry sets into Iceberg-managed tables. Then, validate queries, optimize partitions, and set up data lifecycle policies. Over time, this approach can reduce costs, improve agility, and enable more sophisticated analysis.

In short, Apache Iceberg bridges the gap between observability data and business intelligence. It offers a consistent, scalable, and secure way to turn logs and metrics into strategic insights. Companies that adopt this standard can ask smarter questions faster, helping them respond more effectively to technical issues and business opportunities alike.

Inspired by

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

Sources

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