Combine the power of Apache Spark and Python to build effective big data applications

Key Features

• Perform effective data processing, machine learning, and analytics using PySpark
• Overcome challenges in developing and deploying Spark solutions using Python
• Explore recipes for efficiently combining Python and Apache Spark to process data

Book Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.

You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You'll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

What you will learn

• Configure a local instance of PySpark in a virtual environment
• Install and configure Jupyter in local and multi-node environments
• Create DataFrames from JSON and a dictionary using pyspark.sql
• Explore regression and clustering models available in the ML module
• Use DataFrames to transform data used for modeling
• Connect to PubNub and perform aggregations on streams

Who This Book Is For

The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

Table of Contents

1. Spark installation and configuration
2. Abstracting data with RDDs
3. Abstracting data with DataFrames
4. Preparing data for modeling
5. Machine Learning with MLLib
6. Machine Learning with ML module
7. Structured streaming with PySpark
8. GraphFrames - Graph Theory with PySpark

About the author

Denny Lee is a long-time Apache Spark™ and MLflow contributor, Delta Lake maintainer, and a Sr. Staff Developer Advocate at Databricks. A hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale data platforms and predictive analytics systems. He has previously built enterprise DW/BI and big data systems at Microsoft, including Azure Cosmos DB, Project Isotope (HDInsight), and SQL Server. He was also the Senior Director of Data Sciences Engineering at SAP Concur. He also has a Masters of Biomedical Informatics from Oregon Health and Sciences University and has implemented powerful data solutions for enterprise Healthcare customers. His current technical focuses include Distributed Systems, Delta Lake, Apache Spark, Deep Learning, Machine Learning, and Genomics.

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