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Mastering Hadoop 3

Mastering Hadoop 3

By : Wong, Singh, Kumar
5 (1)
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Mastering Hadoop 3

Mastering Hadoop 3

5 (1)
By: Wong, Singh, 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 (21 chapters)
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1
Section 1: Introduction to Hadoop 3
6
Section 2: Hadoop Ecosystem
10
Section 3: Hadoop in the Real World
16
Section 4: Securing Hadoop

HDFS

HDFS plays a major role in the performance of batch or micro jobs, using HDFS to read and write the data. If there is any bottleneck on the application during writing or reading the file from HDFS, then it will lead to overall performance issues.

DFSIO

DFSIO are the tests that are used to measure read and write performance of MapReduce jobs. They are file-based operations that read and write tasks in parallel. The reduce tasks collect all performance parameters and statistics. You can always pass different parameters to test the throughput, the total number of bytes processed, average I/O, and much more. The important key is that you match these outputs with the number of cores, disks, and memory in your Hadoop cluster...

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