Three case studies of data observability
As the data quality fundamentals shift with data volume, sources, storage, and the desired state, data engineers find it challenging to deliver reliable and high-quality data to the data consumers. Data observability helps them augment their efforts to build trust in data. Three interesting case studies describe how predictive, continuous, self-service data quality and observability help deliver data health.
Unlock the whitepaper to learn:
- What data engineers do
- How data observability empowers data engineers to address the challenges of manual and reactive data quality
- How Fortune 500 companies are leveraging Collibra Data Quality & Observability to drive data health
The shifts in data quality are urging data engineers to rethink their approach to delivering high-quality data pipelines. Data observability empowers them to track the health of enterprise data systems and predict issues before they happen.
Data engineers work on the infrastructure required to deliver reliable and high-quality data to data consumers. As the data quality fundamentals shift with data volume, sources, storage, and the desired state, data engineers find it challenging to deliver healthy data pipelines.