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- Big Data: Principles and best practices of scalable realtime data systems
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- About this Book
Big Data: Principles and best practices of scalable realtime data systems
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Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. Preview — Big Data by Nathan Marz. Big Data: Principles and best practices of scalable realtime data systems by Nathan Marz ,. James Warren. Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database.
As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines wor Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database.
Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team.
Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built. Also available is all code from the book. Get A Copy. Paperback , pages. Published May 10th by Manning first published January 1st More Details Other Editions 2. Friend Reviews. To see what your friends thought of this book, please sign up. To ask other readers questions about Big Data , please sign up. Lists with This Book.
Community Reviews. Showing Average rating 3. Rating details. Sort order. Start your review of Big Data: Principles and best practices of scalable realtime data systems. Feb 11, Szymon rated it it was ok Shelves: big-data. The first chapter is definitely worth reading. Maybe second one too. The rest is way too focused on specific technologies. And so it happens, the technologies happen to be created by the authors. Too much advertising, not enough of the big picture. View 1 comment.
May 08, Sebastian Gebski rated it really liked it. Controversial book. Worst title ever. If it wasn't Nathan Marz father of Storm , I'd never pick it up.
Particular product Kafka, Hadoop, Storm descriptions are Not a deep dive, but author s don't cons Controversial book. Not a deep dive, but author s don't constrain themselves when putting code samples - I don't think it's possible to understand them without external knowledge about these components. It wasn't much of a problem in my case, but may be for others.
And that makes me wondering who is this book made for? In the end, I've enjoyed the content. Jun 06, Bodo Tasche rated it liked it. Sadly not my kind of book. Starting with several examples that use "Gender". From a Gender-Field inside of the database that just knows "Male" and "Female" to an example that tries to guess the "Gender" based on the first name. On top of that it is filled with unnecessary and bad diagrams that basically are explained in one sentence, but the authors thought that it might be good to also put 3 boxes and some arrows between them.
The source code examples are not very good too. With arrows pointing Sadly not my kind of book. With arrows pointing to explain what the code is doing.
One hint: if you need 5 arrows explaining things in a 7 line function, maybe you should try to find a better code example that doesn't need this?
Oct 08, Ulas Tuerkmen rated it it was amazing. It wouldn't be an exaggeration to say that Nathan Marz, as the original developer of Storm together with many other relevant pieces of software, such as Cascalog is among the inventors of the whole Big Data thing.
Storm has enabled complicated real-time pipelines to be built, without the headaches of coordinating data transmissons and routing. It is thus a boon that he, together with James Warren, went on to write a book on the exact same topic, sharing the tips and ideas that went into buildi It wouldn't be an exaggeration to say that Nathan Marz, as the original developer of Storm together with many other relevant pieces of software, such as Cascalog is among the inventors of the whole Big Data thing.
It is thus a boon that he, together with James Warren, went on to write a book on the exact same topic, sharing the tips and ideas that went into building Storm. As such, it is not a surprise that the book is a great overview of the field and fundamental techniques, and has become standard reading already. The demands on a big data system, essentially an OLAP application that has to scale linearly with the amount of input, is fundamentally different from those on an OLTP system, which developers are normally used to developing.
These requirements are robustness also in the face of human error , scalability, modularity and ad hoc queries i. The path taken by many development teams in the face of these differing requirements is the orchestration of existing tools,coupled with simply more code. As the authors point out, this approach leads to overly complex, fragile systems, because the transactional tools were not built for reliable and robust computation, and bring in too much complexity overhead with them.
The alternative is to start with design principles and accompanying family of software tools that give you these requirements from the beginning, when placed into the right design philosophy. The name given by the authors to this design philosophy is Lambda Architecture. The lambda architecture starts with the principle of immutable data. Data is the raw bits and bytes the system receives, and cannot be derived from anything else.
It is at the beginning of the information dependency chain, so to say. Turning this data into a useful form and storing it is the job of three different layers of processing: Batch layer, serving layer and speed layer.
The batch layer is responsible for running preprocessing on the original data to turn it into more accessible form. It has to be performant, scalable,and tolerant to human error. These properties are achieved by using simple storage solutions such as the file system, recomputation algorithms on immutable data, and parallel computation. What the batch layer does not need to be is low-latency. The computations are allowed to run over longer periods of time, in the order of tens of minutes,and work on complete sets of raw data.
A central theme of the book,alluded to above already, is avoiding accidental complexity by reducing each layer to the necessary minimum of functionality.
In the batch layer, this translates to keeping data immutable in terms of storage, and using recomputation algorithms to create the batch views. Recomputation algorithms have three advantages compared to incremental ones: They can be faster, error correction is recomputation, and they tend to be simpler.
The obvious choice for batch processing is Hadoop, and it is not any different in this book. The authors go into some detail on storing and processing data on Hadoop using the Pail data partitioning library and the JCascalog data processing and querying library. One of the weaknesses of the book is obvious in this chapter. Hadoop is not a breeze to install, and the code examples in the batch processing chapters are there only for the reading; they are not particularly 'hackable'.
Also, the code is in Java, which might make sense considering the target audience and the fact that Hadoop and the other big data tools are written in it, but it's not the prettiest code to look at. I ended up not even skimming the Java code,since it's not my favorite way of spending time, and just read the textual explanations. The examples picked by the authors unique views per time window with multiple IDs per user and bounce rate analysis are fortunately not too simplistic.
I can imagine that the code examples are relevant for people who use Java to implement similar things. The batch layer processes the mass of incoming data to precompute batch views, condensed data that can be stored and easily combined to generate information that is of interest.
Data is condensed in two senses: Accumulation and correlation. Accumulation is the calculation of measures of data, such as counts or averages, while also filtering those parts that are irrelevant.
[PDF Download] Big Data: Principles and best practices of scalable realtime data systems [Read]
This is an online version of the Manning book Big Data: Principles and best practices of scalable realtime data systems. With liveBook you can access Manning books in-browser — anytime, anywhere. Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling Big Data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you just need to take a different approach. Big Data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.
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Explore a preview version of Big Data right now. Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems.
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