We discussed HDFS blocks and replication in the previous sections. NameNode stores all metadata information and is a single point of failure, which means that no one can use HDFS if NameNode is down. This metadata information is important and can be used to restart NameNode on other machines. Thus, it is important to take multiple backup copies of a metadata file so that, even if metadata is lost from the primary NameNode, the backup copy can be used to restart the NameNode on the same machine or another machine. In this section, we will discuss NameNode metadata files such as fsimage and edit log. We will discuss data integrity further by using checksum and taking snapshots of the directory to avoid data loss and modification.
Mastering Hadoop 3
By :
Mastering Hadoop 3
By:
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)
Preface
Journey to Hadoop 3
Deep Dive into the Hadoop Distributed File System
YARN Resource Management in Hadoop
Internals of MapReduce
Section 2: Hadoop Ecosystem
SQL on Hadoop
Real-Time Processing Engines
Widely Used Hadoop Ecosystem Components
Section 3: Hadoop in the Real World
Designing Applications in Hadoop
Real-Time Stream Processing in Hadoop
Machine Learning in Hadoop
Hadoop in the Cloud
Hadoop Cluster Profiling
Section 4: Securing Hadoop
Who Can Do What in Hadoop
Network and Data Security
Monitoring Hadoop
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