A field guide to

Designing Data-Intensive Applications

Twelve curated topics covering the ideas that matter most — replication, partitioning, consensus, and the storage and processing models underneath them — each with a diagram, a real-world example, and a code snippet. Not a substitute for depth, but a fast, memorable route to the big picture.

Part I — Foundations

Reliability, Scalability, Maintainability

The three concerns Kleppmann uses to evaluate every data system decision in the rest of the book.

Data Models: Relational vs. Document vs. Graph

Every data model makes some queries easy and others awkward — the choice shapes how you're allowed to think about your data.

Storage Engines: B-Trees vs. LSM-Trees

The two dominant strategies for turning a stream of writes into something you can read back efficiently.

Encoding & Schema Evolution

Turning in-memory data structures into bytes — and making sure old and new code can still talk to each other.

Part II — Distributed Data

Replication

Keeping copies of the same data on multiple machines — and living with the consequences when they briefly disagree.

Partitioning

Splitting data across many nodes so a dataset (and its query load) can scale beyond what a single machine can hold.

Transactions

The contract a database offers for grouping several operations into one all-or-nothing unit — and what each isolation level actually promises.

The Trouble with Distributed Systems

Why distributed systems are fundamentally unreliable — clocks drift, networks lie, and processes pause for longer than you think.

Consistency & Consensus

The strongest guarantees distributed systems can offer — and the algorithms (quorums, consensus protocols) that make them possible despite unreliable networks.