Why Data Lakes Are Crucial for Observability—and When They’re Not the Answer

What a Data Lake Is

Gartner defines a data lake as a semantically flexible storage repository that can ingest raw data from many enterprise sources and retain it in its original format—files, documents, result sets, tables, binaries (BLOBs), and messages. Because the data is stored unchanged, the same lake can serve multiple downstream use cases such as analytics, machine‑learning, and observability.

Why Data Lakes Benefit Observability

When observability platforms can read from a single, unified repository, they gain:

Broad data coverage : logs, metrics, traces, and custom telemetry can be collected without pre‑defining a schema.

Scalable ingestion and processing : large volumes of raw telemetry can be stored cheaply and later transformed into columnar formats (e.g., Parquet) for batch or interactive queries.

Advanced analysis : raw data enables statistical analysis, anomaly detection, and predictive models that go beyond simple alerting.

OpenTelemetry is frequently cited as the de‑facto standard for extracting telemetry in a vendor‑agnostic way, allowing the same data to be streamed into a lake and simultaneously fed to real‑time dashboards.

Typical Architecture Pattern

A common pipeline looks like:

Telemetry → OpenTelemetry Collector → Cloud storage (e.g., S3) → Lambda / Glue → Parquet files → Query engine (Presto/Trino, Athena) → Observability dashboards / ML jobs

In this pattern, the collector normalises data, the serverless functions convert it to an analytics‑friendly format, and the query engine provides low‑latency access for both ad‑hoc investigation and scheduled reporting.

Real‑World Example

CSS Electronics built a data lake for CAN‑bus telemetry:

Vehicle controllers publish raw CAN frames to a message broker.

AWS Lambda functions trigger on each message, transform the payload into Parquet, and write it to an S3 bucket.

AWS Glue crawlers catalog the Parquet files, exposing a unified schema to Athena.

Grafana visualises the Athena tables, giving engineers a single view of vehicle‑level performance and failures.

Limitations and Trade‑offs

Data lakes are not a universal solution. Key drawbacks include:

Latency : Batch conversion and cataloguing add seconds to minutes of delay, which may be unacceptable for real‑time alerting.

Cost of integration : While raw storage is cheap, building reliable ingestion pipelines, schema management, and access controls can require substantial engineering effort.

Operational complexity : Managing data lifecycle, retention policies, and security across a large lake can become a dedicated operational burden.

Experts such as Richard “RichiH” Hartmann (Grafana Labs) caution that many organisations—especially small teams or those with limited budgets—may achieve better ROI by using purpose‑built observability back‑ends instead of a full‑scale lake.

Alternative Approaches

Instead of a proprietary lake, some vendors recommend post‑processing pipelines that stitch together data on demand using AI/ML tools. This reduces the need for a permanent, high‑volume storage layer while still enabling advanced analytics when required.

Guidelines for Deciding Whether to Adopt a Data Lake

Assess data volume and variety: If you need to retain raw, high‑frequency telemetry for months or years, a lake provides cost‑effective storage.

Determine latency requirements: Real‑time monitoring should continue to use low‑latency back‑ends; the lake can serve as a secondary source for deep‑dive analysis.

Evaluate integration effort: Estimate the engineering resources needed to build collectors, converters, and catalogues versus the value of the insights you expect.

Leverage open standards: Use OpenTelemetry, OpenMetrics, and vendor‑agnostic APIs to avoid lock‑in and simplify future migrations.

Conclusion

Data lakes can dramatically extend the analytical capabilities of observability platforms when paired with open telemetry standards and scalable processing pipelines. However, they introduce latency, integration cost, and operational complexity that may outweigh benefits for many organisations. A careful cost‑benefit analysis—considering data volume, latency tolerance, and available engineering resources—should guide the decision to adopt a data lake for observability.