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⚡ Spark your Big Data mastery — don’t get left behind!
This definitive guide, authored by Apache Spark creators, offers a comprehensive, tutorial-driven approach to mastering Spark 2.0. Covering everything from core APIs and real-time streaming to cluster management and scalable machine learning with MLlib, it equips developers and system admins with the skills to deploy, monitor, and optimize Spark applications confidently in production environments.

























| Best Sellers Rank | #289,491 in Books ( See Top 100 in Books ) #18 in Computer Programming Structured Design #30 in Java Programming #85 in Data Modeling & Design (Books) |
| Customer Reviews | 4.5 out of 5 stars 457 Reviews |
R**Z
Good single source for learning and using Spark in production
This book presents the main Spark concepts, particularly the v2.x Structured API in tutorial fashion using Scala and Python. Much of this information is available piecemeal online, but I found it valuable to have it ordered and explained thoroughly rather than digging through stackoverflow or trying to make sense of the docs. After presenting how Spark works and the Structured and low level RDD APIs, the book helps you deploy, monitor, and tune your application to run on a cluster. There is a detailed section on Structured Streaming explaining windowing and event time processing, plus a section on advanced machine learning analytics.
S**U
Very useful book for exploiting the powerful Spark platform
Apache Spark is a powerful platform for Big Data applications that explores a lot of advanced techniques. The book describes clearly and systematically the Spark architecture and has a lot of outstanding examples that help the reader to become familiar with the rather brilliant Spark programming models. The presentation of the material is excellent and the explanations are quite supportive and help the understanding. It is a very nice book on the very admirable Spark system!
J**N
Good intro text - *not* a recipes book
+s: + Great intro text. + Very detailed with lots of code samples. + ML section is thorough (if limited in depth) + all code is on GitHub :) + conceptual + tuning and optimizations sections -s: - Organization is a little choppy - to understand Structured Streamimg aggregations requires jumping back and forth to aggregations section (for example) - Copy-pasting code samples is annoying. - Kindle for Mac is sucky: resizing windows and adjusting text size breaks the flow, sometimes requiring a restart. Indexing is weird and it ”depaginates” - Could use a few sections in wide vs narrow...
E**M
Better than expected
I wasn’t sure about this book initially but as I started to use spark and read the book in parallel I discovered it explained very well the behind the scene that I needed to understand. I would recommend this to people that already program in other languages such as Python and want to start using pyspark
P**P
What a great way to learn Spark (pyspark for me)
Love the book. It gets hands on right away and give you both scala and python versions of code. I used databricks community version of spark. Some code is wrong. Python is sometimes but rarely missing. Highly recommend this to anyone who is looking to gain knowledge in Spark
A**S
Good condition pre-owned book
Absolutely loved it. The packaging was good, received the book before time, and condition was good.
A**R
Far the best Spark book
Despite big volume - 600 pages this is far the best tech book I have read so far. Very well structured, covers different levels - from beginner to expert, excellent diagrams and code examples.
D**D
Gave up about half way through and switched to a better Spark book.
I find the examples being given in both Scala and Python repetitive, especially for trivial code, you don’t really need to know the language that well to understand what Spark functions are being called. Diagrams aren’t very good, a couple of circles and squares with no labels followed by paragraphs of wordy text to explain it. Doesn’t explain how things work or why you would want to do things a certain way, instead reads more like a general reference book. Gave up about half way through and switched to a better Spark book.
J**N
It's a must-have book for people who need to program in SPARK
This book is well-structured especially for people who are new to SPARK but do not need to set up things himself. From earlier chapters (page 49) readers can start to do some simple work and learn some programming. this is encouraging for people to keep learning.
A**A
Buena introducción para trabajar en SPARK y clarificar conceptos
Explicaciones claras, con muchos ejemplos en Scala y Python. Temario actualizado a 2018 y por eso no está basado en RDD, aunque también hay un capítulo para ellos.
J**C
Very good ressource to learn Spark
This is THE book to read if you want practical hands-on on Spark. I really enjoy it practical step-by-step approach. I definitely recommand it to you. Thanks to the authors.
F**O
Libro obligado
Sin duda el mejor libro para comprender cómo funciona el framework de Apache Spark y lo que puedes llegar a hacer con él. Los ejemplos con código e incluso lo que intentan explicarte en las ilustraciones son por demás claros y concisos. Qué mejor que comprar un libro en donde uno de los autores (Matei Zaharia) es uno de los creadores del Framework. Si lo quieren comprar para estudiar y obtener alguna certificación de Databricks, no lo duden, cómprenlo y será la mejor inversión para ese propósito.
A**S
Livro sensacional para quem quer ver as funcionalidades do Spark 2.2.X
Uma das únicas referências para quem quer mais sobre o que há de possibilidades além do Spark 1.6 pois em sua maioria ele aborda temas recentes. O livro é muito claro e objetivo, além de conter diversas referências de mateiras complementares. O autor com toda a certeza domina muito bem o assunto! Livro essencial para que quer entrar no mundo do Spark, com segurança e informações confiáveis, todos exemplos de código são dados tanto em Scala quanto em Python .Único ponto negativo, não do livro mas da Amazon, é que no Brasil não temos a opção de comprar em capa comum, não só para este livro mas para outros que abordam o mesmo tema.
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