For decades, the lack of visibility into the health of our data has led to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail.
This is the only guide of its kind to help data engineers and analysts understand the key factors that contribute to poor data quality and how to detect, resolve, and prevent these issues at scale.
Access your copy to learn:
- Why data quality deserves attention and what exactly is the concept of data downtime.
- How data engineers and analysts can architect more reliable data ecosystems, from ingestion in the warehouse or lake to the analytics layer downstream.
- What it takes to identify, alert for, resolve, and prevent data quality issues in a holistic and end-to-end way across your stack.