data intensive applications martin kleppmann

However, the Stream Processing chapter was very interesting. I am not at all familiar with the concepts of distributed filesystems, MapReduce, and other details discussed in the Batch Processing chapter so I found it a bit hard to keep focus while reading about it. Here Kleppman describes different types of data processing, Batch Processing and Stream Processing. The third, and last, part of the book is called Derived Data and consists of three chapters.

Kleppman describes so many things that can go wrong in a system that you start to wonder how in the world it is possible that things actually do work…most of the time. After reading this part of the book I remembered thinking that it is amazing that we have these big complex systems that actually work. It discusses Replication, Partitioning, Transactions, Troubles with Distributed Systems, and Consistency and Consensus. The second part is called Distributed Data and has five chapters. The details are quite complex and you will most probably not be able to just scim over the pages and expect to be able to follow along. Examples of topics being explained are Reliability, Scalablity, and Maintainability, SQL and NoSQL, datastructures used in databases, different ways of encoding data, and modes of dataflow.

The first part is called Foundations of Data Systems and consists of four different chapters. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.The book is divided into three different parts, each made up of several chapters. Software keeps changing, but the fundamental principles remain the same. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. In addition, we have an overwhelming variety of tools, including NoSQL datastores, stream or batch processors, and message brokers. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability.

Data is at the center of many challenges in system design today.

Data intensive applications martin kleppmann