Amit Nandi, "Spark for Python Developers"
English | ISBN: 1784399698 | 2016 | MOBI/EPUB/PDF (True) | 146 pages | 28 MB
With: Code Files
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. Sparks 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.
Youll 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. Youll explore datasets using iPython Notebook and will discover how to optimize the data models and pipeline. Finally, youll 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