Starting with foundations in SQL and NoSQL, this book explores each "species" chapter by chapter, emphasizing hands?on scenarios and pitfalls to avoid. It then brings everything together in polyglot/hybrid architectures, helping you combine engines confidently, whether you're designing a similarity search with vector databases (ANN), building observability pipelines on time?series data and event logs, modeling relationships in graph databases, or delivering analytics with columnar/OLAP and geospatial queries. The result is a concise, engineer?first map of the database landscape that turns theory into pragmatic decision?making.

What You Will Learn

• Identify the right engine for the job across vector, time?series, graph, columnar/OLAP, geospatial, and event?stream databases
• Evaluate trade?offs in performance, scalability, consistency, storage layout, indexing, and operational complexity
• Design polyglot architectures that combine multiple engines (e.g., vector + OLAP + event?stream) for end?to?end solutions
• Apply use?case patterns for similarity search (ANN), observability metrics, fraud/network analysis, geospatial routing, and analytical reporting
• Understand core internals that matter (index types, retention policies, log?structured storage, spatial indexes, columnar execution) without vendor lock?in
• Avoid common pitfalls in schema design, query planning, and cross?engine data movement
• Build a decision checklist and trade?off tables to justify engine selection to stakeholders

Who This Book Is For

Software engineers; back-end developers; data engineers; system architects; site reliability engineers (SREs); technical leads with basic SQL/NoSQL experience who need practical guidance to select, compare, and combine specialized databases for real?world applications