Today’s data pipelines are growing in both volume and complexity, with an increasing number of data sources and dependencies between data assets. Gartner estimates that data engineering comprises 80% to 90% of the work organizations do with data (source: Gartner – Data Engineering Essentials, Patterns and Best Practices | May 2021).
With so much hinging on having good, reliable data in place, data engineering teams need to be able to detect and resolve data issues as early on as possible.
Data observability meets those challenges, not only by identifying problems, but by providing the context necessary to solve and prevent issues from recurring.
In this guide, we’ll help you approach proactive data observability, covering the essentials of:
- What is data observability
- Challenges you’ll face on the path to data observability
- 5 steps to achieve proactive data observability
- Why proactive data observability matters