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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

Points to remember

We have covered the HDFS in detail and the following are a few points to remember:

  • HDFS consists of two main components: NameNode and DataNode. NameNode is a master node that stores metadata information, whereas DataNodes are slave nodes that store file blocks.
  • Secondary NameNode is responsible for performing checkpoint operations in which edit log changes are applied to fsimage. This is also known as a checkpoint node.
  • Files in HDFS are split into blocks and blocks are replicated across a number of DataNodes to ensure fault tolerance. The replication factor and block size are configurable.
  • HDFS Balancer is used to distribute data in an equal fashion between all DataNodes. It is a good practice to run balancer whenever a new DataNode is added and schedule a job to run balancer at regular intervals.
  • In Hadoop 3, high availability can now have more...
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