Files have been in data processing for decades. A file acts as a persistent storage for data, and programmers have been using files for exchanging or storing data for a long time now. There are formats associated with each file that tell you how data is being written and read from the file. For example, if we consider the .csv file, we may assume that each record is separated by a line and each column is separated by a delimiter. If the writer does not put data in the specific agreed format, then the reader may read data wrongly or processing logic may break. Each file format has a specific way of storing the data and there are different parameters that can help you decide which file format is best suited for your application. In this section, we will walk you through some of the widely used file formats and their internals.
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
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
