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title = "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems - Martin Kleppmann"
date = 2018-01-19
[taxonomies]
tags = ["books", "martin kleppmann", "reviews", "it", "big data"]
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[GoodReads Summary](https://www.goodreads.com/book/show/34626431-designing-data-intensive-applications):
Data is at the center of many challenges in system design today. Difficult
issues need to be figured out, such as scalability, consistency, reliability,
efficiency, and maintainability. In addition, we have an overwhelming variety
of tools, including relational databases, NoSQL datastores, stream or batch
processors, and message brokers. 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. Software keeps changing, but the
fundamental principles remain the same. 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.
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{{ stars(stars=3) }}
First off, right out of the bat: If you want to design Data Intensive
Applications, this is *not* the book you're looking for. This book goes greats
lengths to explain how already existing Data Intensive Applications work --
say, how Zookeeper works when synching data, how Cassandra works without a
leader, how PostgreSQL do transactions and so on.
While informative, the biggest problem is that most of the text is very
loaded: there are layers and layers on each paragraph and you'll take a long
time putting it all together.
Personaly, I felt it lacked examples. Sure, it's interesting how many ways you
can do leader election, but which databases use this or that way? I can see
that one way is the way I want to build my applications on top, but without a
really good example, where should I look?
Also, there is a slight tendency to describe the "market winners" in way more
detail than everything else. There are long discussions about the ways
Cassandra solves its problems than Voldermort (obviously, there is a reason
why Cassandra is the market winner, but this "over-focus" on certain
applications is tiring and just do a job on keeping those on top -- because
that's the ones the book talks and who will look at a database called
Voldermort when you mention it just in passing?)
Overall, it felt like reading my old "Operating Systems 101" books again -- in
a theorical way, not productive way.