A concise guide to implementing Spark Big Data analytics for Python developers, and building a real-time and insightful trend tracker data intensive app
About This Book
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
Who This Book Is For
This book is for data scientists and software developers with a focus on Python who want to work with the Spark engine, and it will also benefit Enterprise Architects. All you need to have is a good background of Python and an inclination to work 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
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.
Style and approach
This is a comprehensive guide packed with easy-to-follow examples that will take your skills to the next level and will get you up and running with Spark.