We have looked at different formats supported by HDFS in Chapter 3, Deep Dive into the Hadoop Distributed File System. We covered many formats including SequenceFile, Map File, and the Hadoop Archive format. We will look at more formats now. The reason why they are covered in this section is because these formats are not used by Apache Hadoop or HDFS directly; they are used by the ecosystem components. Before we get into the format, we must understand the difference between row-based and columnar-based databases because ORC and Parquet formats are columnar data storage formats. The difference is in the way the data gets stored in the storage device. A row-based database stores data in row format, whereas a columnar database stores it column by column. The following screenshot shows how the storage patterns differ between these types:
...Apache Hadoop 3 Quick Start Guide
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Apache Hadoop 3 Quick Start Guide
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Overview of this book
Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS.
The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems.
The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring.
You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark.
By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)
Preface
Free Chapter
Hadoop 3.0 - Background and Introduction
Planning and Setting Up Hadoop Clusters
Deep Dive into the Hadoop Distributed File System
Developing MapReduce Applications
Building Rich YARN Applications
Monitoring and Administration of a Hadoop Cluster
Demystifying Hadoop Ecosystem Components
Advanced Topics in Apache Hadoop
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