How can you use a data validation framework to collaborate with other stakeholders?

1 Why use a data validation framework?

A data validation framework can help you improve your data engineering workflow and deliver more reliable and trustworthy data products to your stakeholders. This can be achieved by catching and fixing data quality issues before they affect downstream tasks and results, as well as defining and enforcing data quality expectations with data providers and consumers. Additionally, you can monitor and alert on data quality metrics and anomalies, while tracking the performance and health of your data pipelines and systems. Lastly, a data validation framework can help you document and share your logic and results, providing evidence for your data decisions and actions.

2 How to choose a data validation framework?

When choosing a data validation framework, there are many factors to consider. These include the type, format, and size of your data sources and targets, and the complexity, frequency, and latency of your data transformations and validations. Additionally, you should think about the level of automation, integration, and customization you need for your data validation workflow. Furthermore, consider the output, storage, and distribution of your data validation results. Popular open-source and commercial frameworks include Great Expectations, Deequ, Soda SQL, Pandera, and DQ Tools. Evaluating these features will help you choose the best framework for your needs.

3 How to use a data validation framework?

Using a data validation framework involves several steps. You must first define the data quality expectations and rules, such as schema, format, range, uniqueness, completeness, consistency, etc., for each data source and target. Then you need to apply the rules to your data by using the framework's methods and functions. This will generate data validation results like success or failure, statistics, errors, warnings, etc. You should store and access these results using the framework's backend and frontend options such as databases, files, APIs, and dashboards. Lastly, you can analyze and act on the results with tools like reports, notifications, logs, and actions. Note that depending on the framework you choose, there may be different options and syntaxes for these steps.

4 How to collaborate with a data validation framework?

A data validation framework can help you collaborate with other stakeholders by enabling you to share your data quality expectations and rules, provide access to the validation results, involve them in your workflow, and learn from their experiences. To ensure successful collaboration, you should use a framework that supports common data formats and standards, integrates with your data platforms and tools, and enables easy communication and documentation.

5 Tips and best practices for using a data validation framework

To get the most out of using a data validation framework, you should start with simple and basic data quality expectations and rules, and gradually increase complexity and detail as you learn more about your data and stakeholders' needs. It's important to test and validate your data quality expectations and rules on sample and representative data, and compare with other sources and methods. Regularly review and update your data quality expectations and rules to account for changing data and business requirements. Automate and orchestrate your data validation workflow, using triggers, schedules, and dependencies to run validations at the right time. Additionally, document and annotate your data quality expectations, rules, and results with clear naming conventions.

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?