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Spark for Python Developers, by Amit Nandi
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Key Features
- Set up real-time streaming and batch data intensive infrastructure using Spark and Python
- Deliver insightful visualizations in a web app using Spark (PySpark)
- Inject live data using Spark Streaming with real-time events
Looking for a cluster computing system that provides high-level APIs? Apache Spark is your answer―an open source, fast, and general purpose cluster computing system. Spark's multi-stage memory primitives provide performance up to 100 times faster than Hadoop, and it is also well-suited for machine learning algorithms.
Are you a Python developer inclined to work with Spark engine? If so, this book will be your companion as you create data-intensive app using Spark as a processing engine, Python visualization libraries, and web frameworks such as Flask.
To begin with, you will learn the most effective way to install the Python development environment powered by Spark, Blaze, and Bookeh. You will then find out how to connect with data stores such as MySQL, MongoDB, Cassandra, and Hadoop.
You'll expand your skills throughout, getting familiarized with the various data sources (Github, Twitter, Meetup, and Blogs), their data structures, and solutions to effectively tackle complexities. You'll explore datasets using iPython Notebook and will discover how to optimize the data models and pipeline. Finally, you'll get to know how to create training datasets and train the machine learning models.
By the end of the book, you will have created a real-time and insightful trend tracker data-intensive app with Spark.
What you will learn- Create a Python development environment powered by Spark (PySpark), Blaze, and Bookeh
- Build a real-time trend tracker data intensive app
- Visualize the trends and insights gained from data using Bookeh
- Generate insights from data using machine learning through Spark MLLIB
- Juggle with data using Blaze
- Create training data sets and train the Machine Learning models
- Test the machine learning models on test datasets
- Deploy the machine learning algorithms and models and scale it for real-time events
Amit Nandi studied physics at the Free University of Brussels in Belgium, where he did his research on computer generated holograms. Computer generated holograms are the key components of an optical computer, which is powered by photons running at the speed of light. He then worked with the university Cray supercomputer, sending batch jobs of programs written in Fortran. This gave him a taste for computing, which kept growing. He has worked extensively on large business reengineering initiatives, using SAP as the main enabler. He focused for the last 15 years on start-ups in the data space, pioneering new areas of the information technology landscape. He is currently focusing on large-scale data-intensive applications as an enterprise architect, data engineer, and software developer. He understands and speaks seven human languages. Although Python is his computer language of choice, he aims to be able to write fluently in seven computer languages too.
Table of Contents- Sales Rank: #78394 in Books
- Published on: 2015-12-24
- Released on: 2015-12-24
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .47" w x 7.50" l, .80 pounds
- Binding: Paperback
- 206 pages
About the Author
Amit Nandi
Amit Nandi studied physics at the Free University of Brussels in Belgium, where he did his research on computer generated holograms. Computer generated holograms are the key components of an optical computer, which is powered by photons running at the speed of light. He then worked with the university Cray supercomputer, sending batch jobs of programs written in Fortran. This gave him a taste for computing, which kept growing. He has worked extensively on large business reengineering initiatives, using SAP as the main enabler. He focused for the last 15 years on start-ups in the data space, pioneering new areas of the information technology landscape. He is currently focusing on large-scale data-intensive applications as an enterprise architect, data engineer, and software developer. He understands and speaks seven human languages. Although Python is his computer language of choice, he aims to be able to write fluently in seven computer languages too.
Most helpful customer reviews
6 of 6 people found the following review helpful.
it is great that author is utilizing some great tech from the ...
By TDL
I really want to give this book five stars, but it has two significant drawbacks. First off, it is absolutely clear that the author is deeply knowledgeable about the subject matter & the tech involved. Also, the author's approach is rock solid in my opinion; I am a big fan of using projects to learn underlying tech. Finally, it is great that author is utilizing some great tech from the PyData world & the Spark world.
The drawbacks, however, are significant. First, the code as presented (at least in the ebook) is in serious need of formatting. The way that the code is presented adds an unnecessary layer of confusion. I'm all for iteratively updating the code as we proceed, but it has to be clear what is happening. The lack of good formatting just tripped me up.
Second, it is not clear that this book was well edited. The author does a great job not assuming much prior knowledge and does a great job in explaining concepts, jargon, and acronyms. I also like the graphics used to visualize ideas and architecture. There are, however, inconsistencies. One example that jumps to mind is in Chapter 3 when shifting from writing the scripts to harvesting data from Twitter (scripts that do not seem to have been tested) to experimenting with Blaze. At the end of "Harvesting and storing data" section it seems as though we will be working with the JSON data, but when we start the "Exploring data using Blaze" section the author is using csv data.
These inconsistencies in the book are huge drawbacks. The blame here should reside with the publisher, since the author has compiled some great material with a great approach to learning the subject matter. If these two drawbacks were fixed, I would happily up this book to 5 stars.
Edit: Why isn't the code available online? Github & Bitbucket both have free public repos. Doesn't make much sense nowadays.
0 of 0 people found the following review helpful.
About everything and nothing in particular.
By Dimitri K
I hoped to learn some details about how to work with Spark in Python, and was disappointed. The author knows some Databases, SQL, some Python, but not too much about Spark. It was a waste of money.
The book is about everything and nothing in particular. Put together some basic principles, some general talk, some screen shots, some diagrams, some Python code, some Twitter API examples, plus wide margins, and you collect enough pages to make a book. Add hot topic "Spark" to the title, and here you are, you have a publication.
0 of 0 people found the following review helpful.
This is just terrible.
By Jan-Samuel Wagner
I don't often leave reviews but I just felt compelled to do so with this book. Here are just a few of my thoughts:
1) The code examples are unreadable.
2) The author skims over important topics in order to squeeze more topics in.
3) The author skims over important details in setting up the environments he describes.
4) Very little of the first few chapters actually deal with Spark, except for some brief examples that are not explained in detail.
Summary: The editors should have taken more time to make sure this is a quality publication. This is just terrible.
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