Data Warehouses & Lakehouses
OLTP vs OLAP, columnar storage, MPP architecture, and the lakehouse's bet on open table formats — the systems layer every data engineering interview eventually asks you to reason about
A practitioner's tour of the storage and compute architectures that sit underneath every analytics stack: why OLTP systems and analytical workloads want fundamentally different engines, what columnar storage buys you and why it's the single biggest lever behind fast aggregate queries, the MPP conceptual model that Snowflake, BigQuery, and Redshift all implement in different ways, and the lakehouse's core bet — that open table formats (Iceberg, Delta Lake, Hudi) can bring warehouse-grade ACID transactions, time travel, and schema evolution to plain object storage. Closes with a decision framework for warehouse vs lakehouse vs plain data lake, and a comparison of how Snowflake, BigQuery, Redshift, and Databricks actually position against each other.
Practice questions (5)
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Diagnosing a Slow Analytics Replica
Intermediate · Free -
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A Warehouse Bill Doubled Overnight — Diagnose the MPP Cost Spike
Intermediate -
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Debugging a Concurrent-Write Conflict on an Iceberg Table
Intermediate -
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Choosing an Architecture for a Growing Data Platform
Intermediate -
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Evaluating a Proposed Redshift-to-Snowflake Migration
Intermediate