Data Quality Tools Comparison for Continuous Data Imports

Explore open-source solutions for ensuring data quality in continuous import scenarios in this 28-minute presentation from Databricks. Compare popular options like Apache Griffin, Deequ, DDQ, and Great Expectations across dimensions such as maturity, documentation, extensibility, and features including data profiling and anomaly detection. Learn about various data quality approaches, tools, and frameworks, including ETL processes, quality checks, code generation, and advanced uniqueness checks. Gain insights into the limitations of Apache Griffin and discover how to implement timely data quality assurance in your organization's data pipeline.

Intro
Data Quality
ETL Process
Quality Checks
Data Quality Approaches
Data Quality Tools
Deku
Code Generation
Great Expectations
Pandas Profiling
Apache Griffin
Apache Griffin Limitations
Examples
Uniqueness checks
Advanced checks
Timely data
Other frameworks