In this blog, we’ll discuss all things data quality and data observability. We’ll present interviews with leading data scientists, and we’ll discuss our own working building data observability solutions for data engineers and data scientists working in a broad range of industries.
Data engineering teams need to be able to find and fix data quality problems quickly. In part 2 of our interview with Michal Klos, Michal discusses the challenges of discovering and troubleshooting data quality problems in pipelines. Michal is Sr. Director of...
Here’s what we’re writing on Medium:
The Data Quality Gap (June 11, 2020)
4 Lessons for Data Scientists from the UK’s A-Levels Algorithm Debacle (August 18, 2020)
Feel free to reach out to us!
Subscribe to the Data Culpa Newsletter