What's inside — all 10 chapters

Why Database Choice Matters

The real cost of a wrong decision — migrations, licensing lock-in, operational overhead, and talent constraints. Includes a $40M real-world case study.

The CAP Theorem — A Foundation for Every Choice

Consistency, Availability, Partition Tolerance — what each means in practice. CP vs AP databases mapped to real use cases, plus the PACELC extension.

OLTP — Relational & SQL Databases

Deep dive into ACID, B-Tree indexes, and WAL. Side-by-side comparison of PostgreSQL, MySQL, Oracle, SQL Server, CockroachDB, and Amazon Aurora.

OLAP & Analytical Workloads — Data Warehouses

Columnar storage, MPP architecture, and the Lakehouse vs Data Warehouse paradigm. Covers Snowflake, Databricks, Redshift, BigQuery, and ClickHouse.

JSON & Document Workloads

Schema flexibility trade-offs and when they hurt. MongoDB architecture, Oracle AJD, Couchbase, and DynamoDB — including a partition key design warning.

Caching — Speed as a Feature

Redis data structures, Memcached simplicity, and Oracle TrueCache. All five caching strategies (cache-aside, write-through, write-behind, read-through, refresh-ahead) explained with trade-offs.

Other Workload Patterns

Graph databases (Neo4j, Neptune), time-series (InfluxDB, TimescaleDB), search engines (Elasticsearch, OpenSearch), and NewSQL (CockroachDB, Spanner).

Architecture & Trade-off Matrix

ACID vs BASE explained. Horizontal vs vertical scaling. A comprehensive side-by-side matrix of all 9 database paradigms across CAP, scaling model, throughput, and complexity.

5-Year Total Cost of Ownership

Detailed cost projections for OLTP, OLAP, and caching workloads. Hidden costs decision-makers miss — egress, index storage, backup, DBA salaries, and training.

Decision Framework

A workload fingerprint checklist, recommended database by use case, a one-page quick-reference summary, and 7 common anti-patterns to avoid — including the top mistakes even experienced teams make.

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