Features
Data observability is the ability to continuously monitor, trace, and troubleshoot data pipelines and assets in real time—much like SRE observability for applications—to ensure data reliability, freshness, and quality.
Data Observability
Data observability extends software-era observability principles to the world of data engineering. By instrumenting every layer of the data stack—storage, processing, transformation, and consumption—teams gain deep, real-time visibility into the health of their datasets and pipelines. This article explains how it works, why it matters, and how to implement it effectively.
Data observability refers to the holistic practice of monitoring, tracking, and understanding the health, quality, and behavior of data as it flows through your systems. Borrowing ideas from application observability (logs, metrics, traces), it adds data-specific signals—such as row counts, schema changes, freshness SLAs, and anomaly detection—to help teams observe their data in production.
Modern companies run on data-driven decision-making and automated analytics. Undetected schema drifts, failed ETL jobs, or silent NULL explosions can corrupt dashboards, ML models, and customer-facing features. Data downtime erodes trust and revenue.
Agile teams deploy code many times a day. That same pace now applies to dbt models, Airflow DAGs, and CDC streams. Without real-time observability, small data issues slip into production unnoticed.
GDPR, HIPAA, and SOC 2 audits demand provable controls over data lineage and access. Observability creates the audit trail.
How up-to-date is each dataset versus its SLA? Lagging data triggers alerts.
Statistical profiles—mean, min/max, unique counts—show whether the data distribution drifts unexpectedly.
Row counts and file sizes reveal partial loads or duplicate spikes.
Changes to column names, types, or order can break downstream code. Observability tools detect and notify instantly.
End-to-end dependency graphs map how raw events propagate into dashboards, letting you assess blast radius when issues occur.
Add lightweight probes to data warehouses (Snowflake, BigQuery), orchestrators (Airflow), and transformation layers (dbt) to emit metadata.
Central observability platforms—open-source (OpenTelemetry, OpenLineage) or commercial (Monte Carlo, Databand)—ingest the signals into a metadata store.
Rules or ML models flag anomalies: schema drift, unusual NULL rates, freshness violations.
Interactive lineage graphs and query logs help engineers trace an incident to the failing upstream job or commit.
Fix the pipeline, add tests, or update SLAs; feed lessons into CI/CD to prevent recurrence.
Suppose the orders table adds a new payment_method column. Downstream reports that SELECT specific columns suddenly fail. A data observability platform automatically:
Instrument the datasets powering executive dashboards or customer features first; expand coverage iteratively.
Store freshness windows, volume thresholds, and anomaly policies in version control alongside pipeline code.
Use chat-ops integrations (Slack, MS Teams) to pipe alerts into runbooks and on-call rotations.
Combine observability with data unit tests (Great Expectations, dbt tests) to catch problems before deploy.
Tests catch known issues at build time; observability catches unknown issues at run time—both are required.
True observability provides explanations (traces, lineage), not just charts.
Data incidents cost startups precious trust and engineering hours; a lightweight observability stack scales down, too.
Galaxy’s modern SQL editor naturally complements data observability:
Start small—instrument one DAG and one warehouse. Track time-to-detection and time-to-resolution metrics to quantify ROI. Then expand coverage and automate remediation.
Without observability, broken data silently propagates, eroding trust in dashboards, ML models, and customer-facing features. Data observability supplies the real-time signals and lineage context required to detect, triage, and fix data incidents quickly—much like SRE observability does for application outages. It shortens data downtime, satisfies compliance requirements, and empowers teams to move faster with confidence.
Monitoring shows you whether a metric crosses a threshold; observability provides the context (schema changes, lineage, query traces) to understand why it happened and how to fix it.
Galaxy surfaces query execution metadata, keeps run histories, and lets teams endorse trusted queries. This in-editor context complements broader observability platforms by catching issues at the moment of query creation.
You can start with open-source libraries like OpenLineage and Great Expectations, but commercial platforms add anomaly detection, lineage graphs, and alert routing out of the box—saving engineering time.
Properly implemented probes are lightweight, often querying metadata tables or sampling rows. Performance impact is minimal compared to the cost of data downtime.
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