No need to spend hours ploughing through endless data - let Spark, one of the fastest big data processing engines available, do the hard work for you.
Key Features
Get up and running with Apache Spark and Python
Integrate Spark with AWS for real-time analytics
Apply processed data streams to machine learning APIs of Apache Spark
Book DescriptionProcessing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. This book teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming.
You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption.
By the end of this book, you'll not only have understood how to use machine learning extensions and structured streams but you'll also be able to apply Spark in your own upcoming big data projects.
What you will learn
Write your own Python programs that can interact with Spark
Implement data stream consumption using Apache Spark
Recognize common operations in Spark to process known data streams
Integrate Spark streaming with Amazon Web Services (AWS)
Create a collaborative filtering model with the movielens dataset
Apply processed data streams to Spark machine learning APIs
Who this book is forData Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics. Although you don't need any knowledge of Spark, prior experience of working with Python is recommended.
Key Features
Get up and running with Apache Spark and Python
Integrate Spark with AWS for real-time analytics
Apply processed data streams to machine learning APIs of Apache Spark
Book DescriptionProcessing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. This book teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming.
You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption.
By the end of this book, you'll not only have understood how to use machine learning extensions and structured streams but you'll also be able to apply Spark in your own upcoming big data projects.
What you will learn
Write your own Python programs that can interact with Spark
Implement data stream consumption using Apache Spark
Recognize common operations in Spark to process known data streams
Integrate Spark streaming with Amazon Web Services (AWS)
Create a collaborative filtering model with the movielens dataset
Apply processed data streams to Spark machine learning APIs
Who this book is forData Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics. Although you don't need any knowledge of Spark, prior experience of working with Python is recommended.
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