Best 32 Data Quality Tools for Amazon Redshift (2026)

Managed agentic data platform built on OpenMetadata that connects to Redshift with a database user, reads SVV_TABLE_INFO and query history (STL_QUERY, STL_SCAN, or SYS_QUERY_HISTORY) for lineage, and runs profiler and data quality tests.

Open-source context platform that connects to Redshift with a database user, reads SVV_TABLE_INFO and query history (STL_QUERY, STL_SCAN, or SYS_QUERY_HISTORY) for lineage, and runs profiler and data quality tests.

Agentic data trust platform that connects to Redshift over JDBC with a username and password, or the IAM role of its processing engine, then browses and profiles tables.

Enterprise AI trust platform that connects to Redshift with a dedicated user granted SYSLOG ACCESS UNRESTRICTED and SELECT on svv_table_info, profiling tables to recommend metrics and running them.

Data and AI observability control plane that connects to Redshift with a database user, or an IAM role on Redshift Serverless in beta, reading SVL_STATEMENTTEXT for lineage on provisioned clusters and running monitors.

Data observability, quality, and lineage platform that connects to Redshift with a user granted SYSLOG access and SELECT on information_schema and schemas, running profiling and data quality monitors.

Managed data and AI control plane by Boomi that connects to Redshift with a dedicated user that reads the Elementary schema plus table metadata and query history, without read access to your tables.

Self-hosted data observability platform with value-level data validation, column-level lineage, a data catalog, and a business glossary.

Data + AI observability platform that connects to Redshift with a user granted SYSLOG ACCESS UNRESTRICTED, reads svv_table_info for freshness and volume and query logs for lineage, and runs validation and custom SQL monitors.

Autonomous data and AI platform that connects to Redshift with a user or AWS IAM (instance profile or IRSA), reading svv_table_info, stl_scan, and stl_query for freshness and query analysis.

Data quality control layer that connects to Redshift with a database user or an assumed IAM role with an external ID, then discovers, profiles, and scans tables on a schedule.

Open-source data quality operations center that connects to Redshift over JDBC with a database user, imports tables, and runs profiling checks and daily monitoring checks on them.

Data observability platform by Datadog that connects to Redshift with a user granted SYSLOG ACCESS UNRESTRICTED, reading svv_table_info for row counts and monitoring the schemas it can SELECT.

Agentic data quality and observability platform that connects to Redshift with a user granted SYSLOG ACCESS UNRESTRICTED and SELECT on monitored tables, running validators on a polling schedule.

Data quality product by Coalesce, built on SYNQ, that connects to Redshift as a dedicated user, reading SVV and pg_catalog views for the catalog and SYS_QUERY_HISTORY when query logs are enabled, usually every 30 minutes.

Enterprise data observability platform that connects to Redshift with a user granted SELECT on tables and svv_table_info, scanning schemas hourly, daily, or weekly to run monitors.

AI-based data observability platform that connects to provisioned Redshift clusters, not Serverless, with a database user, and builds monitored assets from tables or SQL queries.

Open-source data quality testing tools that connects to Redshift with a database user and pushes read-only SQL into Redshift to profile, test, and monitor tables.

Open-source dbt package by Boomi that connects to Redshift with a dedicated user that reads the Elementary schema plus table metadata and query history, without read access to your tables.

AI-powered data operations center with data and pipeline observability, cross-platform pipeline lineage, and automated root-cause analysis.

Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.

Zero-code data quality software where AI agents auto-discover rules, validate and reconcile data, and score data trust across sources.

AI-native platform combining data observability, data quality checks, and business context, with anomaly detection, end-to-end lineage, and pipeline observability.

AI-native data quality platform built on data contracts that connects to Redshift with a username and password, or AWS access keys with an optional role ARN, and runs quality scans on the configured schema.

Open-source data contracts engine that connects to Redshift with a username and password, or AWS access keys with an optional role ARN, and runs quality scans on the configured schema.

AI-powered data engineering platform that connects to Redshift with a user that can SELECT the chosen schemas and keeps data diff scratch tables in a datafold_tmp schema.

AI data review agent for dbt pull requests that connects to Redshift with a username and password and queries base and current environments directly to diff the data a pull request changes.

Open-source command-line tool for ODCS data contracts that tests Redshift tables with a username and password, or IAM authentication with cluster or workgroup settings.

Data product marketplace that connects to Redshift through the Redshift Data API with IAM access keys, syncing schemas, tables, and columns as assets on a schedule.

Open-source Python data quality framework that connects to Redshift with a redshift+psycopg2 connection string and validates tables or SQL queries as batches of data.

Data tests built into open-source dbt Core that run as SQL in Amazon Redshift through the dbt-redshift adapter: generic tests such as unique and not_null, custom SQL tests, and unit tests.

Managed AWS data quality service built on Deequ, with DQDL rulesets, rule recommendations, and anomaly detection for data lakes and Glue pipelines.