DE foundations — OLTP vs OLAP, data modeling, expert SQL¶
Every tool in the data engineering survey chapter — Spark, Kafka, dbt, Snowflake, Iceberg — exists to move data between exactly two kinds of database workload, and almost every production incident in analytics traces back to someone confusing them. This chapter builds the foundations that the rest of the data engineering track stands on: why transactional and analytical systems are physically different (down to the bytes-per-query arithmetic), how to model data for each side (normalization for writes, dimensional modeling for reads, Data Vault for the audit trail in between), and the expert-level SQL — window functions, recursive CTEs, query plans, analytical indexing — that separates someone who can query a table from someone who can design and debug a warehouse. …
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