Automated Data Quality Checks Using Dagster and Great Expectations: A Comprehensive Guide
In today’s data-driven world, ensuring data quality is crucial for businesses looking to make informed decisions based on accurate and reliable information. As data volumes continue to grow and sources become more diverse, manual quality checks are no longer practical or efficient. This is where automated data quality checks come into play, offering a scalable solution to maintain data integrity and reliability.
At my organization, we have implemented a robust system for automated data quality checks using two powerful open-source tools: Dagster and Great Expectations. These tools have become the backbone of our data quality management approach, allowing us to validate and monitor our data pipelines at scale.
Dagster, an open-source data orchestrator, is used for ETL, analytics, and machine learning workflows. It enables data scientists and engineers to build, schedule, and monitor data pipelines efficiently. On the other hand, Great Expectations is a data validation framework that provides validations based on schema and values, helping to ensure data quality and reliability.
Automated data quality checks are necessary for several reasons. They help maintain data integrity, minimize errors, improve efficiency, enable real-time monitoring, and ensure compliance with regulations. By implementing automated data quality checks, businesses can make more informed decisions, avoid costly mistakes, and build trust in their data-driven workflows.
In our organization, we employ different testing strategies for static and dynamic data. Static fixture tests are used for data that is not scraped in real-time, while dynamic fixture tests are conducted on real-time scraped data. Dynamic coverage tests, on the other hand, go a step further by checking data quality without the need to control the profile, using defined rules and constraints.
To help you understand how to implement automated data quality checks using Dagster and Great Expectations, we have provided practical insights and a demo project in a Gitlab repository. The demo project includes steps for generating a data structure, preparing and validating data, and generating expectations for data validation.
In conclusion, data quality is essential for accurate decision-making and avoiding errors in analytics. By combining tools like Dagster and Great Expectations, businesses can automate data quality checks within their data pipelines, ensuring reliability and trust in their data-driven workflows. With a robust data quality process in place, compliance is ensured, and insights derived from data are more trustworthy and valuable.
If you have any further questions about Dagster, Great Expectations, or data quality in general, feel free to refer to our Frequently Asked Questions section for more insights and information. Thank you for reading!