References: Modern Databases and Lakehouses

1. NoSQL - Wikipedia - Detailed survey of NoSQL database categories including key-value, document, column-family, and graph stores. Directly maps to the six database categories taught in the chapter.

2. Data lakehouse - Wikipedia - Coverage of the lakehouse architecture combining data lake flexibility with warehouse-style governance. Foundation for the chapter's lakehouse content.

3. CAP theorem - Wikipedia - Clear treatment of consistency-availability-partition tolerance tradeoffs that drive modern database selection. Anchors the workload-fit decisions covered in this chapter.

4. Designing Data-Intensive Applications - Martin Kleppmann - O'Reilly - The definitive modern reference on database internals, replication, partitioning, and stream processing; a load-bearing source for any chapter on contemporary data systems.

5. NoSQL Distilled - Pramod J. Sadalage and Martin Fowler - Addison-Wesley - Concise comparison of the NoSQL database categories and their use cases; a perfect length match for the breadth-over-depth coverage in this chapter.

6. MongoDB Manual - MongoDB - Authoritative documentation for the leading document database, including data modeling, indexing, and aggregation patterns covered in this chapter.

7. Apache Cassandra Documentation - Apache Software Foundation - Reference for the canonical column-family store, including its data model and write-optimized architecture.

8. Redis Documentation - Redis - Documentation for the dominant key-value store, covering caching patterns, sessions, and data structures relevant to operational workloads.

9. Databricks Lakehouse Platform - Databricks - Vendor explanation of the lakehouse pattern, Delta Lake, and the unification of data warehouse and data lake architectures.

10. Choosing a Database on AWS - Amazon Web Services - Practical decision guide for selecting among relational, key-value, document, column-family, graph, and time-series databases on a major cloud platform.