Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

Micro-batch processing case study


This section covers a small case study that is used to detect an IP default with Kafka and Spark Streaming, and the IP has attempted to hit the server many times. We will cover the following use cases:

  • Producer: The Kafka producer API will be used to read a log file and publish documents on the topic of Kafka. In a real case, however, we could use the flume or producer application, which records in real time directly and publishes on Kafka.
  • Fraud IPs list: We will keep a list of predefined IP frauds to identify the IPs for fraud. We use an in-memory IP list for this application, which can be substituted by fast key-based searching, such as HBase.
  • Spark Streaming: Spark Streaming applications can read Kafka records and detect suspicious IPs and domains.

Maven is a tool for building and managing projects and we will build this project using Maven. Eclipse or IntelliJ are recommended for project creation. Add to your pom.xml the following adjustments and plugins...