Designing Data-Intensive Applications really exceeded my expectations. Even if you are experienced in this area this book will re-enforce things you know (or sort of know) and bring to light new ways of thinking about solving distributed systems and data problems. It will give you a solid understanding of how to choose the right tech for different use cases.

The book really pulls you in with an intro that is more high level, but mentions problems and solutions that really anyone who has worked on these types of applications have either encountered or heard mention of. The promise it makes is to take these issues such as scalability, maintainability and durability and explain how to decide on the right solutions to these issues for the problems you are solving. It does an amazing job of that throughout the book.

This book covers a lot, but at the same time it knows exactly when to go deep on a subject. Right when it seems like it may be going too deep on things like how different types of databases are implemented (SSTables, B-trees, etc.) or on comparing different consensus algorithms, it is quick to point out how and why those things are important to practical real-world problems and how understanding those things is actually vital to the success of a system.

Along those same lines it is excellent at circling back to concepts introduced at prior points in the book. For example the book goes into how log based storage is used for some databases as their core way of storing data and for durability in other cases. Later in the book when getting into different message/eventing systems such as Kafka and ActiveMQ things swing back to how these systems utilize log based storage in similar ways. Even if you have prior knowledge or even have worked with these technologies, how and why they work and the pros and cons of each become crystal clear and really solidified. Same can be said of it's great explanations of things like ZooKeeper and why specific solutions like Kafka make use of it.

This book is also amazing at shedding light on the fact that so little of what is out there is totally new, it attempts to go back as far as it can at times on where a certain technology's ideas originated (back to the 1800s at some points!). Bringing in this history really gives a lot of context around the original problems that were being solved, which in turn helps understanding pros and cons. One example is the way it goes through the history of batch processing systems and HDFS. The author starts with MapReduce and relating it to tech that was developed decades before. This really clarifies how we got from batch processing systems on proprietary hardware to things like MapReduce on commodity hardware thanks in part to HDFS, eventually to stream based processing. It also does great at explaining the pros and cons of each and when one might choose one technology over the other.

That's really the theme of this book, teaching the reader how to compare and contrast different technologies for solving distributed systems and data problems. It teaches you to read between the lines on how certain technologies work so that you can identify the pros and cons early and without needing them to be spelled out by the authors of those technologies. When thinking about databases it teaches you to really consider the durability/scalability model and how things are no where near black and white between "consistent" vs "eventually consistent", these is a ton of nuance there and it goes deep on things like single vs multi leader vs leaderless, linearizability, total order broadcast, and different consensus algorithms.

I could go on forever about this book. To name a few other things it touches on to get a good idea of the breadth here: networking (and networking faults), OLAP, OLTP, 2 phase locking, graph databases, 2 phase commit, data encoding, general fault tolerance, compatibility, message passing, everything I mentioned above, and the list goes on and on and on. I recommend anyone who does any kind of work with these systems takes the time to read this book. All 600ish pages are worth reading, and it's presented in an excellent, engaging way with real world practical examples for everything.

In today’s world, many of us have been tasked with building reliable, scalable services. Yet, more often than not, we rely on existing abstractions without fully understanding how the underlying systems work. Need a scalable database? Use MongoDB. Need a streaming service? Kafka is your go-to. While these tools get the job done, they often serve as crutches that prevent us from delving into the complexities of distributed systems. This book, Designing Data-Intensive Applications, is an eye-opener for anyone who has ever wondered about the internals of these services. It takes a deep dive into key concepts like consistency, exploring the critical differences between strong and weak consistency, and the trade-offs that come with each approach. For example, when a master node fails, how does a new master get elected? The book explains this process in depth, shedding light on the mechanics of fault tolerance. The book also provides clarity on how databases store and retrieve data efficiently. If you’ve ever come across PostgreSQL’s documentation and wondered, "What exactly is a B-tree?", this book will make it crystal clear.

It also goes into the common gotchas when working with transactions. You might think that using transactions makes you safe from concurrency issues, but that’s not always the case. The book explains why this happens and offers practical advice on how to avoid race conditions.

What this book isn’t: If you’re a practitioner building distributed systems from scratch and looking for in-depth explanations of algorithms like Raft, Paxos, or other low-level details, this book might not be what you’re looking for. It serves more as a high-level introduction to distributed systems rather than a deep dive into the specifics of consensus algorithms. For those looking for more detailed, foundational material on distributed systems, I’d recommend checking out Tanenbaum’s Distributed Systems.

DDIA is easily one of the best tech books of 2017 (possibly this decade) and is destined to become a classic. The book deals with all the stuff that happens around data engineering : storage, models, structures, access patterns, encoding, replication, partitioning, distributed systems, batch & stream processing and the future of data systems (don't expect ML because it is a different beast).

Kleppman has coherently blended the relevant computer science theory with modern use cases and applications. The focus is primarily on the core principles and thought-processes that one must apply when it comes to building data services. Design concepts don't go out-of-date soon, so the book has very long shelf-life.

The high-point of this book is the author's lucid prose, which indicates mastery of the subject matter and clarity of thought. Conceptualizing reality is an art and the author really shines here. You’ll find that whenever you have a question after reading a particular sentence, the answer to that will be found in the upcoming sentences. It’s like mind-reading.

Also kudos to the author for those nice diagrams and interesting maps (and for avoiding mathematical formulas with Greek symbols). The bibliography at the end of each chapter is thorough enough for unending personal research.

If you are working on or interviewing for big data engineering, systems design, cloud consulting or devops/SRE, then this book is a keeper for a long-long time.

This book dives deep into its subject with clear structure and thoughtful explanations. The concepts are well-articulated and build on each other logically. However, to truly appreciate the depth and get the most out of it, I recommend reading it with some prior experience or familiarity with the topic. Overall, a highly valuable and rewarding read.

This is a very well written and practical book covering data intensive application as the title claims. Since "data outlives code", this is a must read for any one in technology leadership roles. The topics of consistency, consensus, reliability are handled very well without going on the tangent of complexity in distributed applications (most data applications are distributed in nature). At times the author and editors did not pay attention to crisp presentation and editing in the later chapters.

It is really hard to overstate how comprehensively this book covers nearly everything that is currently known about building large, scalable, high performance, data centric applications. If every Kafka queueing, Cassandra clustering, Redis loving, Kinesis slinging, Map reducing, CAP theorem quoting systems engineer read this book, the world would actually be a better place. It really is that good.

The front pages contain a quote from Alan Kay that I will summarize as "most people who write code for money ... have no idea where [their culture came from]." This book will learn you some of the culture you are missing! Every developer writing modern Internet facing application software, particularly in cloud computing environments, will run into the problems described here. Far too many of these developers will pick up a grab bag of half baked solutions from reading various Stack Overflow posts, blogs from better informed writers, and from hyped up claims made by the currently trendy "technologies." Many of these sources will obscure the fundamental nature of the underlying problems, and will lead said developers to overly naive designs, and provide a false sense of security. Such systems will even work pretty nicely for a while, but they will usually fail spectacularly when they are actually presented with component failures or high system load (or both at the same time, which is quite typical).

This book talks about the underlying structure of the problems we all face when building contemporary distributed applications. It ties together all the foundational aspects of both distributed computing and data storage in a chorent manner. It teaches you how to think about the problem space by demonstrating where many popular and widely used software products fit. You're not going to learn about any one single product. Instead you will learn what you must know to evaluate as many of them as you want, learn which interactions between different components matter, and then make informed choices for your own design.

This is not an academic text book, it is a working professional's guide to the field. It has all the references to the classic papers and textbooks that form the formal foundations of the subject, But it is so clearly written, and accessible, that you could go a very long way without needing to read any of them.

Start learning :)

Imagine that your coding project is like being stuck on a battlefield at night, trying to cut your way through a maze of barbed wire. The average software book is like a flashlight: it illuminates the immediate problem and helps you figure out how to solve it. This book, in contrast, is like a flare shot high into the air. For a brief few moments, it illuminates the entire battlefield, and you can see your lines behind you, the positions of the enemy trenches and fortifications, and even roads, fields and forest in the distance. You're still going to need the flashlight for getting the job done, but getting perspective is invaluable for figuring out where you are and where you're going.

If all you want to know is the minimum needed to do your job _today_, you probably won't like this book, because it covers too much territory to teach you the details of how to do any one thing. However if you want to understand how your current objective fits into the bigger landscape (and why), this book is one of the best overviews I've ever read, for any discipline. Kleppmann has a deep knowledge of the fundamentals and he writes in lucid, simple prose so that anyone can understand the concepts. There's a surprising amount of nitty-gritty detail about things like replication and sharding in distributed databases, and once you grasp the concepts, you'll be well-equipped to judge for yourself how any system fits your needs.

Much of what one reads online about database software has the flavor of commercial advertising or semi-religous feuding among disciples of one system or another. So for me it was a real gift to have Kleppmann explain how technical details distingish data systems from each other, why the system designers made those choices, and what strengths and weaknesses result from them. I highly recommend the book.

This book covers pretty much all topics which are relevant to managing databases or designing data models in more than 800 pages. It also provides detailed information about the inner workings of databases to the degree that you might be able to implement your own simple database.

The book is very well didactically structured which is no surprise given that the author is a professor at Cambridge. For example, it explains batch processing algorithms (e.g. Map Reduce) and uses this as basis to delve into data streaming. Strong emphasis is laid on the problems with regards to distributed computing (replication, partitioning, node failures, etc.) and the discussion of the compromises one must make.

Overall, an easy recommendation for anyone is interested in data architectures and the inner workings of databases which are the backbone of pretty much any application in today’s world.

Se siete IT appassionati del vostro lavoro e volete capire cosa c'è sotto le cose che usate quotidianamente, questo è un libro da non perdere. Non è un manuale, non è una guida né un tutorial, ma fa fede al sottotitolo: è un "viaggio" nello scibile sulla gestione computerizzata di dati, che aiuta a comprendere al di là del marketing gli strumenti che abbiamo a disposizione.

Il libro è densissimo (come dimostra un bell'indice analitico di 30 pagine su un totale di quasi 600), ricco di riferimenti (come dimostrano le folte bibliografie al termine di ogni capitolo, per lo più risorse online) ed è evidente il background accademico dell'autore. E' un libro che richiede tempo nella lettura e comprensione - se non si saltano i dettagli, si intende... ma nel caso lasciate perdere.

Una buona metà del libro riguarda la modifica concorrente di dati e i sistemi distribuiti, la parte più terrificante e affascinante, dove vengono minuziosamente spiegati i problemi che presentano e gli algoritmi che li risolvono (ad esclusione dei problemi "bizantini"). Ho trovato ..."confortante" l'analisi dell'acronimo ACID :)

Chiude con un'analisi di ciò che l'autore si aspetta per il futuro; molto interessante il concetto di "unbundling" dei database.

One of those rare books where the author has clearly taken the time to deeply understand the subject and now knows how to pass that understanding on to the reader. If a question comes to mind while reading, it will most likely be answered shortly. You’ll also find answers to questions you probably wouldn’t have thought to ask.

The book provides solid insights into managing data, particularly at scale. It helped clarify concepts I'd been grappling with and offered practical perspectives that go beyond surface-level explanations. Would recommend for anyone looking to deepen their understanding of large-scale data systems.

Recommend book for software Engineers