Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Enterprise Data Architecture Principles
Enterprise Data Architecture Principles
Data architecture principles
The importance of metadata
Data governance
Data security
Data as a Service
Evolution data architecture with Hadoop
Summary
2
Hadoop Life Cycle Management
Hadoop Life Cycle Management
Data wrangling
Data masking
Data security
Summary
3
Hadoop Design Consideration
Hadoop Design Consideration
Understanding data structure principles
Installing Hadoop cluster
Exploring HDFS architecture
Introducing YARN
Configuring HDFS high availability
Configuration of HA NameNodes with QJM
Hadoop cluster composition
Best practices Hadoop deployment
Hadoop file formats
Summary
4
Data Movement Techniques
Data Movement Techniques
Batch processing versus real-time processing
Apache Sqoop
Flume
Apache NiFi
Kafka Connect
Summary
5
Data Modeling in Hadoop
Data Modeling in Hadoop
Apache Hive
Supported datatypes
How Hive works
Hive architecture
Hive data model management
JSON documents using Hive
Apache HBase
Summary
6
Designing Real-Time Streaming Data Pipelines
Designing Real-Time Streaming Data Pipelines
Real-time streaming concepts
Real-time streaming components
Apache Storm
Other popular real-time data streaming frameworks
Apache Flink versus Spark
Apache Spark versus Storm
Summary
7
Large-Scale Data Processing Frameworks
Large-Scale Data Processing Frameworks
MapReduce
Hadoop MapReduce
Apache Spark 2
Summary
8
Building Enterprise Search Platform
Building Enterprise Search Platform
The data search concept
The need for an enterprise search engine
Elasticsearch
How to index documents in Elasticsearch?
Mapping
Elasticsearch-supported data types
Analyzer
Logstash
Kibana
Use case
Summary
9
Designing Data Visualization Solutions
Designing Data Visualization Solutions
Data visualization
Practical data visualization in Hadoop
Summary
10
Developing Applications Using the Cloud
Developing Applications Using the Cloud
What is the Cloud?
Available technologies in the Cloud
Planning the Cloud infrastructure
Building a Hadoop cluster in the Cloud
Data access in the Cloud
Summary
11
Production Hadoop Cluster Deployment
Production Hadoop Cluster Deployment
Apache Ambari architecture
Setting up a Hadoop cluster with Ambari
Hadoop clusters
Summary

Hive architecture

The following is a representation of Hive architecture:

The preceding diagram shows that Hive architecture is divided into three parts—that is, clients, services, and metastore. The Hive SQL is executed as follows:

  • Hive SQL query: A Hive query can be submitted to the Hive server using one of these ways: WebUI, JDBC/ODBC application, and Hive CLI. For a thrift-based application, it will provide a thrift client for communication.
  • Query execution: Once the Hive server receives the query, it is compiled, converted into an optimized query plan for better performance, and converted into a MapReduce job. During this process, the Hive Server interacts with the metastore for query metadata.
  • Job execution: The MapReduce job is executed on the Hadoop cluster.
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End of Section 5
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