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Architecting for Data Quality in the Lakehouse with Delta Lake and PySpark
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- 1 Intro
- 2 Welcome
- 3 Introductions
- 4 Agenda
- 5 Data Quality Cone of Anxiety
- 6 How do we address bad data
- 7 What is data observability
- 8 Freshness
- 9 Distribution
- 10 Volume
- 11 Schema
- 12 Data Lineage
- 13 Data Reliability Lifecycle
- 14 Lake vs Warehouse
- 15 Metadata
- 16 Storage
- 17 Query logs
- 18 Query engine
- 19 Questions
- 20 Describe Detail
- 21 Architecture for observability
- 22 Measuring update times
- 23 Loading data in CSV or JSON
- 24 Update cadence
- 25 Feature engineering
- 26 Lambda function
- 27 Delay between updates
- 28 Model Parameters
- 29 Training Labels
- 30 Questions and Answers
- 31 Summary
- 32 Upcoming events
- 33 Data Quality Fundamentals
- 34 Monte Carlo
