Designing Data-Intensive Applications
# Outline
“Designing Data-Intensive Applications” by Martin Kleppmann covers a wide range of topics related to data-intensive systems design, but some of the main sections and chapters include:
Introduction
- Overview of data-intensive systems and the challenges they pose
- The importance of data storage, data processing, and data transportation
Data Models and Query Languages
- Relational data models and SQL
- Document data models and query languages
- Graph data models and query languages
Storage and Retrieval
- File storage and the filesystem
- Block storage and the storage stack
- Column-family storage and NoSQL databases
- Time-series databases
Data Processing
- Batch processing: MapReduce, Hadoop, and Spark
- Stream processing: Kafka, Storm, and Flink
- Event-time and watermarks
- Dataflow and directed acyclic graphs
Data Integration
- Data replication: one-to-one, one-to-many, and many-to-many
- Data federation and data sharding
- Data warehousing and data lakes
- Change data capture and event sourcing
- Atomicity, consistency, isolation, and durability
- Two-phase commit and three-phase commit
- Distributed transactions and consensus
- Eventual consistency and conflict resolution
Replication and Partitioning
- Leader-based replication
- Quorum-based replication and Paxos
- Raft and Multi-Paxos
- Sharding and partitioning
Conclusion
- Summary of key concepts and best practices
- Future directions for data-intensive systems
This book is a comprehensive guide that covers the most important topics in data-intensive systems design. It provides a deep understanding of the principles and techniques for designing scalable and fault-tolerant systems.