Emerging as a layer in the modern data stack just over a year ago, data observability refers to an organization’s ability to fully understand the health and reliability of the data in their system. Traditionally, data teams have relied on data testing alone to ensure that pipelines are resilient; in 2023, as companies ingest ever-increasing volumes of data and pipelines become more complex (LLMs, any one?), this approach is no longer sufficient.
If you’re in data, dealing with broken pipelines, missing rows, and duplicate data (as well as the complications and frustrations that come with data downtime) is probably a familiar experience, even with testing.
Fortunately, new approaches have emerged over the past few years to supplement testing, most notable, automated data observability.
In this video, we highlight the TL;DR of data observability and discuss when it makes sense to implement this critical piece of software for your modern data stack.