Data Warehouse Query History and Job Metadata

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Query history and job metadata let agents explain warehouse cost, latency, failures, and data access without guessing.

## Core Explanation

Data warehouses expose operational metadata about submitted jobs, executed queries, users, timestamps, bytes scanned, durations, and errors. Agents can use that evidence to identify expensive queries, failed jobs, stale dashboards, and unexpected access patterns.

Agents should treat query history as sensitive operational data. A useful answer cites the warehouse, job ID, time window, user or service account, and query text handling policy. It should not expose private query text unless the workflow explicitly permits it.

## Source-Mapped Facts

- BigQuery documentation describes INFORMATION_SCHEMA.JOBS views as containing metadata about BigQuery jobs. ([source](https://cloud.google.com/bigquery/docs/information-schema-jobs))
- Snowflake documentation describes QUERY_HISTORY as an Account Usage view for query history. ([source](https://docs.snowflake.com/en/sql-reference/account-usage/query_history))
- Amazon Athena documentation describes viewing recent queries in query history. ([source](https://docs.aws.amazon.com/athena/latest/ug/querying-query-history.html))

## Further Reading

- [BigQuery INFORMATION_SCHEMA JOBS](https://cloud.google.com/bigquery/docs/information-schema-jobs)
- [Snowflake QUERY_HISTORY](https://docs.snowflake.com/en/sql-reference/account-usage/query_history)
- [Amazon Athena Query History](https://docs.aws.amazon.com/athena/latest/ug/querying-query-history.html)