Webinar: Test Coverage: The Software Development Idea That Supercharges Data Quality & Data Engineering

In an exciting webinar, we talk about the importance of having test coverage across all your tables and tools

Written by Chris Bergh on July 17, 2025

DataOpsData QualityDataOps TestGenOn-Demand WebinarOpen Source
Webinar: Test Coverage: The Software Development Idea That Supercharges Data Quality & Data Engineering

Key points

  • Test coverage in data means automated checks on raw data and on data that a tool or piece of code has acted on. Spot checks, ad hoc looks, and a colleague who knows the business saying "that looks right" are not coverage.
  • Two things need covering at once: production, where the code is static and the data keeps changing, and development, where the data is static and the code keeps changing. Many of the same tests serve both.
  • Shift left means catching a data problem earlier in production; shift down means catching a code problem earlier in development. Both follow the 1:10:100 rule — a dollar at the source, ten in a report, a hundred in front of a customer.
  • Full coverage has two halves: every table and every column at every level of the database, plus every business metric someone has memorized, and every tool checked for errors and for timing.
  • Rules of thumb: two tests per table, two per column, at least one custom test per significant metric. In the three-layer worked example that is roughly 2,500 tests — about seven months of work at 30 minutes each, which is why they have to be generated rather than hand-written.
  • Data lineage is the blueprint of the building; a Data Journey with tests is the fire alarm panel. Lineage says five tables could be affected — coverage tells you only one of them actually failed.

In software engineering, test coverage is non-negotiable. So why do most data teams still ship data without knowing what’s tested—and what isn’t? Explore how leading data teams are applying the proven discipline of test coverage to data and analytics—automating quality checks across every table, not just the “important” ones. Whether you’re battling silent data failures, burned-out engineers, or quality issues that only show up in production, this webinar will give you a new way to think about trust in your data stack. You’ll see how a structured approach to data test coverage can catch issues before stakeholders do.

You’ll learn:

TIP

For those who enjoy reading, we also have a blog post that delves into the ideas in more detail.

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Chris Bergh

Chris Bergh

CEO and Head Chef at DataKitchen. He is a leader of the DataOps movement and is the co-author of the DataOps Cookbook and the DataOps Manifesto.

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