Looking for the best data quality tools for Apache Spark? This list covers 14 tools that natively integrate with Apache Spark — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its Apache Spark integration documentation so you can evaluate support.
By Ari Bajo - Data Engineer turned Writer.
See the full data quality tools list
All 36 data quality, data testing, and data observability tools.
Open-source Python library with declarative expectations to validate data in files, SQL databases, data warehouses, and in-memory DataFrames.
Best for data engineering teams looking for a code-first OSS data testing library with a large built-in expectation library and Python extensibility.
Data quality framework for Apache Spark with data quality rule generation from profiling results, and YAML and Python-based data validation checks.
Best for Databricks users looking to validate PySpark DataFrames and Tables across Spark Core, Spark Structured Streaming, and Lakeflow Pipelines / DLT.
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.
Automated data quality monitoring platform with UI-based anomaly detection tests for structured and unstructured data.
Best for data teams looking for a specialized data quality monitoring tool that integrates with specialized and cloud-native data catalog tools.
Open-source data quality testing and observability platform with data quality checks, monitors, data lineage with Marquez, and data quality dashboards.
Best for data teams looking to customize built-in data quality checks and data quality dashboards with Looker Studio to monitor data quality KPIs.
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.
Data pipeline and data warehouse monitoring platform with job pipeline monitors, data monitors, and task-based data lineage.
Best for data teams looking for end-to-end ETL pipeline monitoring with tasks that span across dbt, Airlfow, Spark, IBM DataStage, and IBM Watsonx Data.
Agentic data observability and FinOps platform for the cloud with integrations with external data quality checks, cost optimization, and incident management.
Best for data teams that want to combine in one platform data quality results with costs and performance recommendations.
Managed data quality platform built on the open-source Deequ framework with data quality rulesets, scheduling, data quality dashboards, and anomaly detection.
Best for data teams using AWS Glue Data Catalog and ETL jobs that want to monitor data quality at rest and in transit, with the possibility to quarantine data.
Managed data quality platform with built-in metrics to write data contracts (using YAML, UI, or AI), anomaly detection and AI agents to clean data.
Best for data teams looking to embed data contracts within data pipeline steps, collaborate with business users to fix bad data, and integrate with data catalogs.
Open-source Python library and CLI to write and run data contracts in YAML using SodaCL with integrations for data warehouses, databases and query engines.
Best for data engineering teams looking for a YAML-based OSS data testing library that embeds directly in pipelines and CI/CD workflows.
Shift left data platform with data contracts, static code analysis, and CI/CD integrations pivoting to data compliance.
Best for regulated industries that want to audit sensitive data flows and prevent bad data in tables, files and streams.
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.
Unified data trust platform with data monitoring, pipeline monitoring, a data catalog, column-level lineage, and data access control.
Best for data teams looking to combine data observability, a data catalog, and data governance in the same tool.
Evaluating data quality tools?
Market Guide (7,000 words) · Feature Matrix (73 features) · Integration Matrix (227 integrations)
Join the Newsletter
One email a month — a new tool list, comparison matrix, and market guide, straight to your inbox. Next up: Data Governance, LLMOps, Data Orchestration.
By Ari Bajo - Data Engineer turned Writer.