Book Image

Real-Time Big Data Analytics

By : Sumit Gupta, Shilpi Saxena
Book Image

Real-Time Big Data Analytics

By: Sumit Gupta, Shilpi Saxena

Overview of this book

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.
Table of Contents (17 chapters)
Real-Time Big Data Analytics
About the Authors
About the Reviewer

Performance tuning and best practices

In this section, we will discuss various strategies for optimizing the performance of our Spark jobs. We will also discuss a few best practices with respect to Spark and Spark SQL.

Performance tuning is very subjective and a wide open statement. The very first step in performance tuning is to answer the question, "Do we really need to performance tune our jobs?" Now before we answer this question, we need to consider the following aspects:

  • Are our jobs meeting SLAs specified by the business?

    If yes, then no need for performance tuning.

  • What do we want to achieve and is it realistic?

    For example, expecting all Spark jobs (irrespective of data size or computations performed) to be completed in milliseconds is unrealistic.

Once we answer and define the need for performance tuning, only then should we move ahead and think of the strategy and start identifying areas where we can performance tune our Spark jobs.

Though there is no standard guide for performance tuning...