By Amit Nandi
ISBN-10: 1784399698
ISBN-13: 9781784399696
Key Features
- Set up real-time streaming and batch information in depth infrastructure utilizing Spark and Python
- Deliver insightful visualizations in an online app utilizing Spark (PySpark)
- Inject reside info utilizing Spark Streaming with real-time events
Book Description
Looking for a cluster computing process that offers high-level APIs? Apache Spark is your answer―an open resource, quick, and basic objective cluster computing method. Spark's multi-stage reminiscence primitives supply functionality as much as a hundred instances swifter than Hadoop, and it's also well-suited for computing device studying algorithms.
Are you a Python developer prone to paintings with Spark engine? if this is the case, this publication may be your spouse as you create data-intensive app utilizing Spark as a processing engine, Python visualization libraries, and net frameworks corresponding to Flask.
To start with, you'll research the best strategy to set up the Python improvement atmosphere powered by way of Spark, Blaze, and Bookeh. you are going to then easy methods to hook up with info shops similar to MySQL, MongoDB, Cassandra, and Hadoop.
You'll extend your talents all through, getting familiarized with many of the facts resources (Github, Twitter, Meetup, and Blogs), their information buildings, and recommendations to successfully take on complexities. you are going to discover datasets utilizing iPython laptop and should observe tips to optimize the information versions and pipeline. eventually, you will get to grasp how one can create education datasets and teach the computer studying models.
By the top of the ebook, you've created a real-time and insightful pattern tracker data-intensive app with Spark.
What you are going to learn
- Create a Python improvement setting powered via Spark (PySpark), Blaze, and Bookeh
- Build a real-time pattern tracker facts in depth app
- Visualize the developments and insights won from facts utilizing Bookeh
- Generate insights from information utilizing desktop studying via Spark MLLIB
- Juggle with facts utilizing Blaze
- Create education facts units and teach the desktop studying models
- Test the desktop studying types on attempt datasets
- Deploy the desktop studying algorithms and types and scale it for real-time events
About the Author
Amit Nandi studied physics on the unfastened college of Brussels in Belgium, the place he did his study on computing device generated holograms. computing device generated holograms are the major parts of an optical machine, that is powered by way of photons working on the velocity of sunshine. He then labored with the college Cray supercomputer, sending batch jobs of courses written in Fortran. This gave him a flavor for computing, which saved becoming. He has labored greatly on huge enterprise reengineering tasks, utilizing SAP because the major enabler. He concentrated for the final 15 years on start-ups within the facts house, pioneering new parts of the knowledge expertise panorama. he's at present targeting large-scale data-intensive functions as an company architect, info engineer, and software program developer. He knows and speaks seven human languages. even though Python is his computing device language of selection, he goals in order to write fluently in seven desktop languages too.
Table of Contents
- Setting Up a Spark digital Environment
- Building Batch and Streaming Apps with Spark
- Juggling info with Spark
- Learning from facts utilizing Spark
- Streaming dwell facts with Spark
- Visualizing Insights and Trends
Read Online or Download Spark for Python Developers PDF
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Extra resources for Spark for Python Developers
Example text
The book will be limited to building the virtual machine using VirtualBox. From a data-intensive app architecture point of view, we are describing the essential steps of the infrastructure layer by mentioning scalability and continuous integration beyond just virtualization. Persistence layer The persistence layer manages the various repositories in accordance with data needs and shapes. It ensures the set up and management of the polyglot data stores. It includes relational database management systems such as MySQL and PostgreSQL; key-value data stores such as Hadoop, Riak, and Redis; columnar databases such as HBase and Cassandra; document databases such as MongoDB and Couchbase; and graph databases such as Neo4j.
No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
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