Designing Data-Intensive Applications

Earlier this year the book club of our company has studied excellent book:

Martin Kleppmann - Designing Data-Intensive Applications

This is the best book I have read about building complex scalable software systems. 💪

As usually I prepared an overview and mind-map.

Chapter 2:

What is data model.
Different relations between the data.
Relational, Document, Graph data models. Which one is better and when.
Schema-on-write, schema-on-read (schemaless). Data locality.
Query languages: imperative, declarative, MapReduce. Why NoSQL is reinventing SQL 😀
Storing graphs. Query languages for graphs: Cypher, SPARQL, Datalog.

Earlier this year the book club of our company has studied excellent book:

Martin Kleppmann - Designing Data-Intensive Applications

This is the best book I have read about building complex scalable software systems. 💪

As usually (to better learn) I prepared an overview and mind-map.

Chapter 1:

Building blocks of the apps
What is Reliability, Scalability and Maintainability. Examples and definitions.
- Faults and Failures
- Performance, Load, Latency and Response Time
- Operability, Simplicity, Evolvability
Why you should randomly kill your servers 😅
How Twitter delivers 12,000 tweets per second to 300,000 readers per second. (VERY interesting!)
How much money Amazon loses for each 100ms delay in their response time
How to quickly calculate percentiles for monitoring response time in PROD