Great Expectations
GX Cloud is a managed data quality platform with a library of expectations based on the open-source GX Core Python library, data profiling, scheduling, and anomaly detection.
Best for data teams looking for a mix of UI-managed tests and custom Python workflows to validate a mix of files, SQL databases, data warehouses, and in-memory DataFrames.
Soda
Soda Cloud is a flexible data quality platform based on the open-source Soda Core Python library with built-in metrics to write data tests, monitors, and contracts in YAML, a UI, or AI.
Best for data engineering teams looking to embed tests at every pipeline stage, quarantining failed records, and bi-directional integrations with data governance tools.
AWS Glue Data Quality
Managed data quality platform built on top of the open-source Deequ framework with data quality rulesets, scheduling, data quality dashboards, and anomaly detection.
Best for data teams using the AWS Glue Data Catalog and AWS Glue ETL jobs that want to monitor data quality at rest and in transit, with the possibility to quarantine data.
Google CloudDQ
Cloud-native data validation CLI with YAML-based data quality checks for BigQuery tables and GCS structured data.
Best for data teams looking for a BigQuery-native solution to write reusable SQL checks and consume data quality outputs programmatically.
DQX by Databricks
Data quality framework for Apache Spark with data quality rule generation from profiling results, YAML and Python-based data validation checks, and a data quality dashboard.
Best for Databricks users looking to validate PySpark DataFrames and Tables across Spark Core, Spark Structured Streaming, and Lakeflow Pipelines / DLT.
DataKitchen
Open-source data testing and observability platform with automated test generation, data profiling, and anomaly detection.
Best for data teams looking for a cost-effective data testing and observability solution that also offers DataOps training services.
Monte Carlo
Leading data observability platform with data monitors, anomaly detection, customizable data quality dashboards, and column-level lineage.
Best for data teams with a big budget looking for a mature and customizable data observability platform that also offers AI observability.
DQLabs
Unified data quality and observability platform with anomaly detection, data quality checks, end-to-end data lineage, and pipeline observability.
Best for enterprises looking for unified data quality and observability that integrates with modern data catalogs and issue management tools.
DQOps
Open-source data quality testing and observability platform with data quality checks, monitors, data lineage with Marquez, data quality dashboards, and incident management.
Best for data teams looking to customize built-in data quality checks and data quality dashboards with Looker Studio to monitor data quality KPIs.
Collate
Unified metadata platform built upon the open-source OpenMetadata project with data discovery features, observability metrics, column-level lineage, and governance workflows.
Best for data teams looking for a unified enterprise solution to data discovery, observability, and governance with a wide range of integrations.
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By Ari Bajo - Data Engineer turned Writer.
SelectZero
Comprehensive data observability platform with data validation, data profiling, column-level data lineage, a data catalog, and a business glossary.
Best for enterprises looking for a data quality tool that can be easily self-hosted with a Docker deployment.
Ataccama ONE
Data trust platform with data quality evaluation rules, anomaly detection, data lineage, a data catalog, and master data management.
Best for organizations looking to scale data management initiatives with enterprise master data management, data quality, and data governance.