Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)

Apache Spark versus Storm

Spark uses micro-batches to process events while Storm processes events one by one. It means that Spark has a latency of seconds while Storm provides a millisecond of latency. Spark Streaming provides a high-level abstraction called a Discretized Stream or DStream, which represents a continuous sequence of RDDs. (But, the latest version of Spark, 2.4 supports millisecond data latency.) The latest Spark version supports DataFrames.

Almost the same code (API) can be used for Spark Streaming and Spark batch jobs. That helps to reuse most of the code base for both programming models. Also, Spark supports Machine learning and the Graph API. So, again, the same codebase can be used for those use cases as well.