In Silicon Valley, "ability to code" is now the uber-metric to track. Starting from how engineers are interviewed, actual hands-on work (due to processes that overemphasizes "do" over "think, e.g., daily stand-ups require you to say what concrete thing you did yesterday), evaluation of work ("move fast and break things") to over-emphasizing on downstream "fixes" (prod-ops culture, 24*7 firefighting heroism) - the top echelon of technology gravitated towards things that it can see, feel, measure. What often gets neglected in this "code be all" culture is deep understanding of fundamental concepts, and how most newer "innovations" are indeed built on a handful time-honored principles.
Nowhere else perhaps is this more prominent than in data space that up-levels libraries and frameworks as the conversation starter. That gets in the way of success. It is indeed impossible to model Cassandra "tables" without understanding - at least - quorum, compaction, log-merge data structure. Due to the way the present day solutions are built ("fits one use case perfectly well"), if these solutions are not implemented well to the particular domain, failure is just a release away.
Mr Kleppmann does a great job of articulating the "systems" aspects of data engineering. He starts from a functional 4 lines code to build a database to the way how one can interpret and implement concurrency, serializability, isolation and linearizability (the latter for distributed systems). His book also has over 800 pointers to state of the art research as well as some of the computer science's classic papers. The book slows down its pace on the chapter on Distributed System and on the final one. A good editor could have trimmed about 120 pages and still retain most value one could get from the book.
That said, if you ever worked on data systems, especially across paradigms (IMS -> RDBMS -> NoSQL -> Map-Reduce -> Spark -> Streaming -> Polyglot), this book is pretty much only resource out there to tie the "loose ends" and paint a coherent narrative. Highly recommended!
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Who Should Read This Book?
If you develop applications that have some kind of server/backend for storing or processing data, and your applications use the internet (e.g., web applications, mobile apps, or internet-connected sensors), then this book is for you.
This book is for software engineers, software architects, and technical managers who love to code. It is especially relevant if you need to make decisions about the architecture of the systems you work on—for example, if you need to choose tools for solving a given problem and figure out how best to apply them. But even if you have no choice over your tools, this book will help you better understand their strengths and weaknesses.
You should have some experience building web-based applications or network services, and you should be familiar with relational databases and SQL. Any non-relational databases and other data-related tools you know are a bonus, but not required. A general understanding of common network protocols like TCP and HTTP is helpful. Your choice of programming language or framework makes no difference for this book.
If any of the following are true for you, you’ll find this book valuable:
- You want to learn how to make data systems scalable, for example, to support web or mobile apps with millions of users.
- You need to make applications highly available (minimizing downtime) and operationally robust.
- You are looking for ways of making systems easier to maintain in the long run, even as they grow and as requirements and technologies change.
- You have a natural curiosity for the way things work and want to know what goes on inside major websites and online services. This book breaks down the internals of various databases and data processing systems, and it’s great fun to explore the bright thinking that went into their design.
Scope of This Book
Sometimes, when discussing scalable data systems, people make comments along the lines of, 'You’re not Google or Amazon. Stop worrying about scale and just use a relational database'. There is truth in that statement: building for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. However, it’s also important to choose the right tool for the job, and different technologies each have their own strengths and weaknesses. As we shall see, relational databases are important but not the final word on dealing with data.
This book does not attempt to give detailed instructions on how to install or use specific software packages or APIs, since there is already plenty of documentation for those things. Instead we discuss the various principles and trade-offs that are fundamental to data systems, and we explore the different design decisions taken by different products.
We look primarily at the architecture of data systems and the ways they are integrated into data-intensive applications. This book doesn’t have space to cover deployment, operations, security, management, and other areas—those are complex and important topics, and we wouldn’t do them justice by making them superficial side notes in this book. They deserve books of their own.
Many of the technologies described in this book fall within the realm of the Big Data buzzword. However, the term 'Big Data' is so overused and underdefined that it is not useful in a serious engineering discussion. This book uses less ambiguous terms, such as single-node versus distributed systems, or online/interactive versus offline/batch processing systems.
This book has a bias toward free and open source software (FOSS), because reading, modifying, and executing source code is a great way to understand how something works in detail. Open platforms also reduce the risk of vendor lock-in. However, where appropriate, we also discuss proprietary software (closed-source software, software as a service, or companies’ in-house software that is only described in literature but not released publicly).
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Nilendu Misra
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Great Book for Practitioners and Non Backend Engineers
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Destined to become a classic
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