Open source data quality tools are software applications designed to assess and improve the quality of data within an organization. These tools provide functionalities to identify, measure, monitor, and enhance the overall quality of data assets.
Data quality is an often neglected area of data engineering. Data quality has benefitted dramatically with the boom in FOSS tools in data engineering. Only a few years ago, the only way to test data pipelines, ETL scripts, and general SQL was to use one of the platform-specific tools like Apache Griffin or other heavy-weights like Talend Data Quality.
80% of digital organizations will fail because they don’t take a modern approach — Gartner
The Challenges of Traditional Data Quality Testing
Imperative testing approaches often involve writing extensive code to define each test case. This can lead to:
- Maintenance overhead: Changes to data schemas or business rules require significant code modifications.
- Scalability issues: Managing and executing a large number of tests can become complex.
- Limited reusability: Test logic is often tightly coupled to specific data sources or transformations.
To combat these challenges, Open Source Tools provide a very useful approach-the Declarative framework which addresses not only these challenges but also providing structured assessments, streamline processes, facilitate stability and enable custom rules.
Declarative Testing
Declarative testing focuses on what to test rather than how to test it. This is achieved by defining test rules in a structured, often configuration-based format. This approach offers several advantages:
- Simplified test definition: Rules are expressed concisely which helps to reduce the amount of code required.
- Improved maintainability: Changes to test logic are easier to implement by modifying configuration files.
- Optimized reusability: Rules can be applied to different datasets or environments with minimal changes.
How Datachecks Helps?
Datachecks is an open-source platform that embodies the principles of declarative data quality testing and goes a step further to advance and automate the whole process. Developers and data engineers can implement and scale testing effortlessly through easy to use and integrate.
- name: check_unique_orders_per_day
table: orders
column: order_id
partition_by: order_date
type: uniqueness
description: All order IDs are unique within each day.
This configuration defines a check to ensure that order_id values are unique within each distinct value of the order_date column.
Datachecks supports a wide range of pre-built metrics, including: Reliability, Numeric Distribution, Uniqueness, Completeness, Validity.
Features of Datachecks:
- Open-source: Free to use and extend.
- Ease of use: YAML-based configuration simplifies test definition.
- Scalability: Easily manage and execute a large number of tests.
- Comprehensive Reporting: Generate shareable HTML reports with all metrics with single command.
dcs-core inspect -C config.yaml --html-report. - Secure by design: Datachecks does not store any data.
