Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish 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 (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

Impala


Impala is a modern, open source massive parallel processing (MPP) SQL engine designed to work with a Hadoop environment. It provides the ability to execute queries with low latency. Hive does not meet the expectation for use cases requiring interactive analytics in a multi-user environment. Impala is integrated into the Hadoop environment and uses a number of standard Hadoop components such as Metastore, HDFS, HBase, YARN, and Sentry. Unlike hive, it does not run MapReduce jobs to get results. Hive uses the MapReduce engine for execution and the intermediate output results are stored on disk, which acts as an input to another job. 

Impala architecture

Impala is a massive parallel processing (MPP) distributed query execution engine. It utilizes the resources of an existing Hadoop cluster. It does not use MapReduce. However, it utilizes the data locality feature of Hadoop processing. Let's discuss the Impala architecture and its components in detail.

 

 

The following diagram shows the Impala...