Design, process, and analyze large sets of complex data in real time
About This Book
- Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm
- Implement strategies to solve the challenges of real-time data processing
- Load datasets, build queries, and make recommendations using Spark SQL
Who This Book Is For
What You Will Learn
- Explore big data technologies and frameworks
- Work through practical challenges and use cases of real-time analytics versus batch analytics
- Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm
- Handle and process real-time transactional data
- Optimize and tune Apache Storm for varied workloads and production deployments
- Process and stream data with Amazon Kinesis and Elastic MapReduce
- Perform interactive and exploratory data analytics using Spark SQL
- Develop common enterprise architectures/applications for real-time and batch analytics
Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.
Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.
From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.
Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program.
You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.
At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.
Style and approach
This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features.
Each topic is explained sequentially and supported by real-world examples and executable code snippets.
Table of Contents
Chapter 1. Introducing the Big Data Technology Landscape and Analytics Platform
Chapter 2. Getting Acquainted with Storm
Chapter 3. Processing Data with Storm
Chapter 4. Introduction to Trident and Optimizing Storm Performance
Chapter 5. Getting Acquainted with Kinesis
Chapter 6. Getting Acquainted with Spark
Chapter 7. Programming with RDDs
Chapter 8. SQL Query Engine for Spark – Spark SQL
Chapter 9. Analysis of Streaming Data Using Spark Streaming
Chapter 10. Introducing Lambda Architecture