Overview

Martin Kleppmann’s comprehensive guide to modern data systems is essential reading for anyone building software that handles data at scale. This book bridges theory and practice beautifully.

Part I: Foundations of Data Systems

Reliability, Scalability, Maintainability

The three pillars of good system design:

• Reliability: System works correctly even when things go wrong
• Scalability: Reasonable ways to handle growth
• Maintainability: Making life better for engineering and operations teams

Data Models

Understanding different data models is crucial:

• Relational (SQL)
• Document (MongoDB, CouchDB)
• Graph (Neo4j)
• Column-family (Cassandra)

Each has trade-offs depending on your use case.

Part II: Distributed Data

Replication

Key replication strategies:

1. Single-leader: Simple but can be a bottleneck
2. Multi-leader: Complex but handles network partitions better
3. Leaderless: High availability, eventual consistency

Partitioning

Strategies for distributing data:

• Key-range partitioning
• Hash partitioning
• Consistent hashing

Part III: Derived Data

Batch Processing

MapReduce and beyond - understanding how to process large datasets efficiently.

Stream Processing

Real-time data processing with systems like Kafka, Flink, and Storm.

Key Insights for ML Engineering

This book transformed how I think about ML systems:

1. Feature stores: Understanding distributed data helps design better feature storage
2. Model serving: Applying principles of scalability to ML inference
3. Data pipelines: Building reliable ETL for training data
4. Monitoring: Adapting database monitoring concepts to ML systems

Technical Depth

What I appreciate most is the depth of technical detail while remaining accessible. Kleppmann doesn’t shy away from complexity but explains it clearly with excellent diagrams.

Practical Applications

Applied these concepts to:

• Designing a distributed feature store for ML models
• Improving data pipeline reliability
• Making better technology choices for new projects

A must-read for anyone serious about building data systems. Dense but incredibly valuable. I keep coming back to it as a reference.