English | 2016 | ISBN: 978-1-78439-969-6 | 146 Pages | PDF, EPUB | 14 MB
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
- 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