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A working handbook for data and software engineering.

Hands-on tutorials by Alex Merced on Apache Iceberg, the data lakehouse, pipelines, agentic AI, and the languages and tools that hold it all together.

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  1. 01Dimensional Modeling: Facts, Dimensions, and GrainsDimensional modeling is the most widely used approach for organizing analytics data. Developed by Ralph Kimball, it structures data into two types of tables:...
  2. 02Slowly Changing Dimensions: Types 1-3 with ExamplesDimensions change. A customer moves cities. A product gets reclassified. An employee changes departments. How your data model handles these changes determine...
  3. 03Data Modeling for Analytics: Optimize for Queries, Not TransactionsThe data model that runs your production application is almost never the right model for analytics. Transactional systems are designed for fast writes : in...
  4. 04Denormalization: When and Why to Flatten Your DataNormalization is the first rule taught in database design. Eliminate redundancy. Store each fact once. Use foreign keys. It's the right rule for transactiona...
  5. 05Data Vault Modeling: Hubs, Links, and SatellitesDimensional modeling works well when your source systems are stable and your business questions are predictable. But what happens when sources change constan...
  6. 06Data Modeling Best Practices: 7 Mistakes to AvoidA bad data model doesn't announce itself. It hides behind slow dashboards, conflicting numbers, confused analysts, and AI agents that generate wrong SQL. By ...