How do you use data engineering frameworks and libraries to ensure data quality and governance?

1 Data validation frameworks

Data validation frameworks are designed to help you check and enforce the quality and consistency of your data sources, transformations, and outputs. By allowing you to define data quality rules, metrics, and expectations, and applying them to your data at different stages of your pipeline, they can ensure the accuracy of your data. Popular frameworks include Great Expectations, a Python-based framework that provides data profiling, documentation, and alerting features; Deequ, a Scala-based framework that leverages Apache Spark to perform data quality checks; and Databricks Expectations API, a Python-based framework that integrates with Databricks. All three frameworks enable users to define data quality constraints, measure them using distributed computing capabilities, detect anomalies and suggest repairs.

2 Data testing libraries

Data testing libraries are essential tools for writing and running automated tests for data pipelines and projects. They verify the functionality, performance, and accuracy of ingestion, transformation, or loading processes. Additionally, they help you detect bugs or anomalies in your data or code. Popular data testing libraries include Pytest, a Python-based library with a simple and flexible framework for unit, integration, and end-to-end tests; DBT, a SQL-based framework for testing data transformations and models; and Airflow, a Python-based framework for creating and managing data pipelines with tasks that can be scheduled, monitored, and retried. These libraries also provide features such as plugins, extensions, data documentation, lineage, orchestration, Python operators, sensors, or hooks. Furthermore, they integrate with other testing tools like Great Expectations.

3 Data documentation frameworks

Data documentation frameworks are invaluable tools for generating and maintaining documentation for data assets, such as schemas, definitions, metadata, or lineage. These frameworks provide a single source of truth and a common language for data stakeholders, such as analysts or engineers. Furthermore, they help you comply with data governance policies and regulations, such as data privacy, security, or quality standards. Popular and useful frameworks include Schemacrawler, Dataedo, and Amundsen. Schemacrawler is a Java-based tool that extracts and analyzes metadata from relational databases. It allows you to generate data documentation in various formats and visualize data schemas using diagrams or graphs. Dataedo is a web-based tool that enables you to create and manage data documentation for various sources. It supports features such as defining and documenting data elements and their properties, as well as data lineage, quality, and governance features. Amundsen is an open-source platform that integrates with various data sources. It enables users to create and access data documentation via a web interface or chatbot, while providing features for search, exploration, and collaboration.

5 Data access control frameworks

Data access control frameworks are tools that help you manage and enforce the permissions and policies for accessing your data assets, such as databases, files, or APIs. Apache Ranger, Apache Sentry, and Immuta are some of the most commonly used frameworks. Apache Ranger allows you to define and administer data access policies using a web interface or a REST API, while Apache Sentry allows you to define and enforce data access policies using a web interface or a CLI. Immuta is a cloud-based platform that connects with various data sources and platforms, allowing you to define and apply data access policies using a web interface or a REST API. All of these frameworks help you comply with data governance policies and regulations, such as data security, privacy, or ethics. Additionally, they enable you to monitor and audit data access activities, as well as audit and report data access events using a centralized database or a SIEM system.

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?