AI agents can produce plausible responses even when tool calls or underlying actions fail, making broader testing essential.

This report looks at six major trends in how pipelines and the practices of data engineering are changing to deliver more value and support future agentic AI applications.

Earlier this year, I had the privilege of serving on the organizing committee for the DataTune conference in my hometown of Nashville, Tenn. Unlike many database-specific or platform-specific ...

Today, at its annual Data + AI Summit, Databricks announced that it is open-sourcing its core declarative ETL framework as Apache Spark Declarative Pipelines, making it available to the entire Apache ...

Shifting left is popular in domains such as security, but it is also essential for achieving better test automation for CI/CD pipelines By shifting left, you can design for testability upfront and get ...

Telemetry pipelines may sound like a complex and relatively new concept, but they’ve been around for a long time. Telemetry pipelines play a crucial role in harnessing the power of telemetry data; ...

Using workarounds to pipe data between systems carries a high price and untrustworthy data. Bharath Chari shares three possible solutions backed up by real use cases to get data streaming pipelines ...

Planning for data migration? This guide will help you take the right steps to make sure everything runs smoothly. Data migration testing ensures that all your data sets and system integrity are ...