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Data Observability

Data Observability

Monitor Snowflake end-to-end directly inside Dynatrace.

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Data Observability application main screenDynatrace Snowflake Observability Agent connection configuration wizard
  • Product information
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Overview

The Data Observability application connects Snowflake accounts with Dynatrace through a guided setup wizard, automatically provisioning 14 purpose-built dashboards across five observability domains: security, costs, performance, quality, and operations. All monitoring modules activate out of the box — no manual configuration, no dashboard building from scratch.

End-to-end visibility into your data pipelines and platforms — starting with Snowflake. Databricks, dbt, Airflow, and Azure Data Factory coming soon. End-to-end lineage and BI tool visibility planned.

This is intended for:

  • Organizations monitoring Snowflake warehouse health alongside application observability in Dynatrace.
  • Teams needing credit consumption visibility and cost attribution by warehouse or team.
  • Data engineers and SREs who need to triage query performance issues without context-switching to Snowflake.
  • Those wanting proactive visibility into credit burn, warehouse saturation, and query failures.

Use cases

  • Incident triage for Data Engineers: When query performance degrades or a warehouse is saturated, Data Engineers need to identify the root cause fast — without switching between Snowflake's Query History UI and other tools. The Data Observability app surfaces account-wide execution trends, warehouse-level diagnosis, long-running queries, and resource consumption by user, table, and database — all inside Dynatrace, alongside the applications and infrastructure that depend on Snowflake.
  • Cost governance for Data Platform Leads: Credit overruns often go undetected until a billing alert fires. The app surfaces which warehouses lack resource monitors, tracks quota consumption across the fleet, projects credit exhaustion timelines against configured limits, and breaks down consumption by warehouse and team — giving platform leads the visibility to act before limits are breached.
  • Unified observability for Platform SREs: SREs managing Snowflake alongside application infrastructure no longer need a separate tool for Snowflake security posture, warehouse health, or data freshness. Trust Center findings, authentication anomalies, schema changes, and pipeline freshness are visible in the same observability plane as the rest of the stack.

Get started

Open the Data Observability app and navigate to the Connections view. Click + Connection, select Snowflake, and follow the guided setup wizard. The one-time Snowflake account setup requires administrator access; ongoing monitoring runs on a least-privilege service account — no administrative privilege at runtime.

Details

Includes 14 dashboards available from day one:

  • Security: Trust Center findings, authentication health, failed login monitoring, admin activity, role and privilege changes, and user identity overview.
  • Costs: Resource monitor governance and credit exhaustion forecasting, warehouse efficiency analysis (idle time, auto-suspend, multi-cluster), budgets, and cross-account consumption.
  • Performance: Account-wide query execution trends, warehouse-level diagnosis, long-running query detection, and resource consumption by user, table, and database.
  • Quality: Data freshness monitoring (stale table detection) and schema change audit log.
  • Operations: Warehouse health, Snowpipe ingestion, task graphs, and dynamic table refresh.
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Full version history

ReleaseDate

Full version history

  • Pipeline Explorer is now gated by its own feature flag, independent of the OpenLineage ingestion flag.
  • The Executions tab gained a "Group by root job" switch to move between a grouped, pipeline-per-row view and a flat, most-recent-first list of every run; the active view is preserved in the URL and survives filter changes.
  • The Duration and Failure rate columns on the Jobs and Executions tabs now show inline mini-charts — a trend sparkline for duration and a colour-coded meter for failure rate — instead of plain numbers.
  • Clicking a row in the Jobs tab or either Executions view now opens a details pane with execution and pipeline information and a "View Lineage" action, without leaving the list.
  • Various Executions table polish: Run time is now the first column (including for expanded run rows), expanded rows show the Run ID, and both tables use a condensed row density.
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