compare + contrast

How to choose the right data architecture: Weighing the pros and cons of Data Mesh and Data Fabric.

Learn more about how data quality tests, data monitoring, and data observability differ, while still improving data quality in their own ways.

Data observability is arguably the hottest concept in the data space today, but few have a clear sense of what it means. This blog post offers a definition of data observability, differentiates it from related concepts, and emphasizes why it's important.

Data catalogs are being adopted to improve data quality and help analytics and data engineering teams better understand what's available to use. Read on if you're currently evaluating data catalogs!

A detailed guide comparing Data Mesh and Data Lake architectures, and factors to consider when choosing.

Overview of the similarities and differences between the established field of Software Observability and the emerging category of Data Observability.

Explore the key differences between DevOps and DataOps methodologies and learn how implementing both can drive better business outcomes for data-driven organizations.

How to make the right choice in a market bursting with options

dbt tests are suited for catching known issues, by testing binary criteria on critical tables. Metaplane is suited for catching unknown issues, by testing scalar criteria on a wide range of assets.