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