Table of Contents

• Frameworks and Libraries

  • dbt - assertions) - ELT tools that comes with a handy utility to define tests as SQL queries.
  • Soda
  • Sifflet
  • Validio
  • Lantern
  • Acceldata
  • Marquez
  • deepchecks - tool for validating your machine learning models and data. Implemented test suites tailored towards ML models datasets and outputs.
  • elementary - Data monitoring and observability tailored to dbt.
  • mobydq - tool for data engineering teams to run & automate data quality checks on their data pipeline.
  • ydata-quality - python library for assessing data quality throughout stages of the data pipeline development.
  • great-expectations - tool for data testing, documentation, and profiling.
  • deepqu - libray by Amazon for defining unit tests for data with focus on large datasets. Based on Apache Spark.
  • soda - enables data testing through extended SQL queries.
  • dqm - another data quality monitoring tool implemented using Spark.
  • owl-sanitizer - yet another Spark based lightweight data validation framework.
  • griffin - Data Quality solution for distributed data systems at any scale in both streaming and batch data context.
  • drunken-data-quality
  • DataQuality for BigData
  • TopNotch
  • Phasor Data Quality Tracker
  • DataCleaner
  • data-quality
  • evidently - analyze and track data and ML model output quality.
  • Databand