How do you validate data outputs?

1 Data validation types

Data validation can be classified into two types: schema validation and content validation. Schema validation checks whether the data outputs conform to the expected structure, format, and metadata. For example, you can use schema validation to verify that the data outputs have the correct column names, data types, and constraints. Content validation checks whether the data outputs match the expected values, logic, and rules. For example, you can use content validation to verify that the data outputs have no missing, duplicate, or inconsistent data, and that they satisfy certain business or domain requirements.

2 Data validation methods

Data validation methods can be divided into two categories: manual and automated. Manual validation involves inspecting the data outputs visually or using simple queries or tools. Manual validation can be useful for spot-checking, debugging, or exploring the data outputs, but it is not scalable, consistent, or efficient for large or complex data sets. Automated validation involves using scripts, frameworks, or pipelines to perform predefined checks and tests on the data outputs. Automated validation can be integrated into the data engineering workflow, run periodically or on-demand, and generate reports or alerts on the data quality issues.

3 Data validation tools

Data validation tools can help you implement and manage data validation methods more easily and effectively. Depending on your data sources, formats, and platforms, there are many such tools available. For example, Great Expectations is a Python-based framework that allows you to define, store, and run data quality tests on various data sources, such as relational databases, data lakes, or data warehouses. dbt is another SQL-based framework that enables you to transform, document, and test your data outputs in a modular and reusable way. Deequ is a Scala-based library that provides a domain-specific language to express and measure data quality metrics on large-scale data sets using Apache Spark. Lastly, Airflow is a Python-based platform that orchestrates and monitors data engineering pipelines, including data validation tasks, using directed acyclic graphs.

4 Data validation best practices

Data validation is not an occasional task, but a continuous and essential part of data engineering. To ensure data quality, it’s important to define clear expectations for data outputs, based on the needs of stakeholders. Additionally, you should implement data validation methods and tools that can handle changes and updates in your data outputs. Automating data validation and integrating it into your data engineering pipeline will help ensure consistency, efficiency, and scalability of your data quality checks. Moreover, you should monitor and report data validation results and issues to stakeholders for transparency, accountability, and feedback. Lastly, it’s important to review and improve your data validation methods and tools regularly to ensure continued improvement of your data quality standards.

5 Data validation challenges

Data validation can be tricky in the context of data engineering, as data sources, formats, platforms, and workflows are often diverse, complex, and dynamic. Common challenges include data complexity, variability, volume, and governance. Data outputs may have different levels of granularity, aggregation, or dimensionality which may require various validation methods and tools. As data outputs may change over time due to new sources or formats, frequent updates or revisions of validation methods and tools may be necessary. Additionally, large or distributed data outputs may pose performance or scalability issues for validation methods and tools. Finally, varying owners, users, or policies of data outputs may affect access, control, or responsibility for validating them.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?