We covered:

• Barr’s background
• Market gaps in data reliability
• Observability in engineering
• Data downtime
• Data quality problems and the five pillars of data observability
• Example: job failing because of a schema change
• Three pillars of observability (good pipelines and bad data)
• Observability vs monitoring
• Finding the root cause
• Who is accountable for data quality? (the RACI framework)
• Service level agreements
• Inferring the SLAs from the historical data
• Implementing data observability
• Data downtime maturity curve
• Monte carlo: data observability solution
• Open source tools
• Test-driven development for data
• Is data observability cloud agnostic?
• Centralizing data observability
• Detecting downstream and upstream data usage
• Getting bad data vs getting unusual data

Links:

• Learn more about Monte Carlo: https://www.montecarlodata.com/
• The Data Engineer's Guide to Root Cause Analysis: https://www.montecarlodata.com/the-data-engineers-guide-to-root-cause-analysis/
• Why You Need to Set SLAs for Your Data Pipelines: https://www.montecarlodata.com/how-to-make-your-data-pipelines-more-reliable-with-slas/
• Data Observability: The Next Frontier of Data Engineering: https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/
• To get in touch with Barr, ping her in the DataTalks.Club group or use barr@montecarlodata.com

Join DataTalks.Club: https://datatalks.club/slack.html