Automating Data Quality Checks with Dagster

Data Quality Assurance: Automating Checks with Dagster and Great Expectations

Maintaining high data quality is critical for data-driven businesses. As data volumes and sources increase, manual quality control becomes inefficient and prone to errors. Automated data quality checks offer a scalable solution to preserve data integrity and reliability.

Our organization, a large-scale public web data collector, utilizes a robust automated system built on the open-source tools Dagster and Great Expectations. These tools are central to our data quality management strategy, enabling efficient validation and monitoring of our data pipelines.

This article details our implementation of comprehensive automated data quality checks using Dagster (a data orchestrator) and Great Expectations (a data validation framework). We'll cover the benefits of this approach, providing practical implementation insights and a GitLab demo to illustrate how these tools can improve your data quality assurance.

Before diving into specifics, let's examine each tool.

Key Learning Points:

• Understand the importance of automated data quality checks in data-driven decision-making.
• Learn how to implement data quality checks using Dagster and Great Expectations.
• Explore testing strategies for static and dynamic data.
• Understand the benefits of real-time monitoring and compliance in data quality management.
• Implement a demo project for automated data quality validation.

(This article is part of the Data Science Blogathon.)

Table of Contents:

• Introduction
• Dagster: An Open-Source Data Orchestrator
• Great Expectations: A Data Validation Framework
• The Need for Automated Data Quality Checks
• Data Quality Testing Strategies
• Implementing Automated Data Quality Checks
• Conclusion
• Frequently Asked Questions

Dagster: Orchestrating Data Pipelines

Dagster streamlines the building, scheduling, and monitoring of data pipelines for ETL, analytics, and machine learning workflows. This Python-based tool simplifies debugging, asset inspection, and status/metadata/dependency tracking for data scientists and engineers. Dagster enhances pipeline reliability, scalability, and maintainability, integrating with Azure, Google Cloud, AWS, and other common tools. While alternatives like Airflow and Prefect exist, Dagster offers compelling advantages (easily found through online comparisons).

Great Expectations: A Data Validation Powerhouse

Great Expectations is an open-source platform for maintaining data quality. It uses "Expectations" (assertions about data) to provide schema and value-based validations, including checks for maximum/minimum values and counts. It also validates data and generates expectations based on input data (requiring some adjustment, but saving time). Great Expectations integrates with Google Cloud, Snowflake, Azure, and over 20 other tools. Although it might present a steeper learning curve for non-technical users, its benefits are significant.

Why Automate Data Quality Checks?

Automated quality checks offer numerous benefits for organizations handling large volumes of critical data. For accurate, complete, and consistent information, automation surpasses error-prone manual processes. Here are five key reasons:

• Data Integrity: Establish reliable data using predefined quality criteria, reducing the risk of flawed assumptions and decisions.
• Error Minimization: While errors can't be eliminated entirely, automation minimizes their occurrence and allows for early anomaly detection, saving resources.
• Efficiency: Automation frees data teams from time-consuming manual checks, allowing them to focus on analysis and reporting.
• Real-Time Monitoring: Enables immediate issue detection before they escalate, unlike slower manual checks.
• Compliance: Supports data quality compliance requirements, especially crucial for regulated industries. Automated checks provide verifiable evidence of data quality.

Data Quality Testing Methods

Our approach categorizes tests by data type (static or dynamic) and check type (fixture or coverage).

• Static Fixture Tests: These use pre-saved static fixtures (e.g., HTML files) and compare parser output to expected output. They're run in CI/CD pipelines to detect breaking changes.
• Dynamic Fixture Tests: Similar to static tests, but data is scraped in real-time, verifying both scraper and parser functionality and detecting layout changes. These are scheduled rather than run on every merge request.
• Dynamic Coverage Tests: These use Great Expectations to check data against predefined rules (expectations), regardless of whether the profiles are controlled. This is crucial for data quality assurance across various sources.

Implementing Automated Data Quality Checks

Our GitLab demo showcases the use of Dagster and Great Expectations for data quality testing. The demo graph includes operations such as data loading, structure loading, data flattening, DataFrame creation, Great Expectations validation, and validation result checks.

The demo includes data, structure, and expectations for Owler company data. Instructions for generating your own structure and expectations are provided. The demo demonstrates how to use Dagster to orchestrate the data flow and Great Expectations to perform the validation. The process includes flattening nested data structures to create individual Spark DataFrames for validation.

Conclusion

Various data quality testing methods exist, depending on the pipeline stage. A robust automated system is essential for ensuring data accuracy and reliability. While not strictly required for all testing (static fixture tests, for example), tools like Dagster and Great Expectations significantly enhance data quality assurance. This guide provides valuable insights for improving or establishing data quality processes.

Key Takeaways:

• Data quality is paramount for accurate analytics and preventing costly errors.
• Dagster automates and orchestrates data pipelines, providing monitoring and scheduling.
• Great Expectations offers a flexible framework for defining, testing, and monitoring data quality.
• Combining Dagster and Great Expectations enables automated, real-time data quality checks.
• A strong data quality process ensures compliance and builds trust in data-driven insights.

Frequently Asked Questions:

• Q1: Dagster's purpose? A1: Dagster orchestrates and automates data pipelines for efficient workflows.
• Q2: Great Expectations' role? A2: Great Expectations defines, validates, and monitors data quality expectations.
• Q3: Dagster and Great Expectations integration? A3: Dagster integrates with Great Expectations for automated data quality checks within pipelines.
• Q4: Data quality's importance in analytics? A4: High data quality ensures accurate insights, prevents errors, and improves decision-making.

(Note: Media in this article is used with the author's permission and is not owned by Analytics Vidhya.)

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