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querying-aws-redshift

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Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.

skills/specialized-skills/system-table-skills/querying-aws-redshift/SKILL.md

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# Query AWS Redshift System Tables

## Overview

**Works best with** the [AWS MCP server](https://docs.aws.amazon.com/aws-mcp/) for sandboxed execution and audit logging. All commands below use the AWS CLI and work in any environment with configured AWS credentials. Use IAM roles or temporary credentials; avoid long-lived access keys.

Redshift can publish **system tables** — the `SYS_*` monitoring data such as `sys_query_history`, `sys_query_detail`, and `sys_connection_log` — to **S3 Tables** as continuously-updated Apache Iceberg tables.

Terminology used throughout: **system table** refers to a `SYS_*` dataset generally, and each one maps 1:1 to a published Iceberg table. Where this skill says **`SYS_` view**, it means specifically the live in-cluster object you query on the cluster itself — that is a view, and it is a different thing from the published S3 Tables copy. This applies to both **Provisioned clusters** and **Serverless namespaces**. It is an opt-in extension of the existing logging APIs. Published tables are read-only, stored in the AWS-managed `aws-redshift` table bucket, and queryable via any Iceberg-compatible engine including Amazon Athena and Amazon Redshift itself.

Querying the S3 Tables copy is preferred over the live in-cluster `SYS_` views when analyzing historical or high-volume system-table data because:

- The in-cluster `SYS_` views have a limited retention window; S3 Tables retains history well beyond it.
- Querying S3 Tables adds **no load** to the running Redshift cluster.