If you are an administrator or developer who wants to enter the world of Big Data and BigTables and would like to learn about HBase, this is the book for you.
Summary HBase in Action has all the knowledge you need to design, build, and run applications using HBase. First, it introduces you to the fundamentals of distributed systems and large scale data handling. Then, you'll explore real-world applications and code samples with just enough theory to understand the practical techniques. You'll see how to build applications with HBase and take advantage of the MapReduce processing framework. And along the way you'll learn patterns and best practices. About the Technology HBase is a NoSQL storage system designed for fast, random access to large volumes of data. It runs on commodity hardware and scales smoothly from modest datasets to billions of rows and millions of columns. About this Book HBase in Action is an experience-driven guide that shows you how to design, build, and run applications using HBase. First, it introduces you to the fundamentals of handling big data. Then, you'll explore HBase with the help of real applications and code samples and with just enough theory to back up the practical techniques. You'll take advantage of the MapReduce processing framework and benefit from seeing HBase best practices in action. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside When and how to use HBase Practical examples Design patterns for scalable data systems Deployment, integration, and design Written for developers and architects familiar with data storage and processing. No prior knowledge of HBase, Hadoop, or MapReduce is required. Table of Contents PART 1 HBASE FUNDAMENTALS Introducing HBase Getting started Distributed HBase, HDFS, and MapReduce PART 2 ADVANCED CONCEPTS HBase table design Extending HBase with coprocessors Alternative HBase clients PART 3 EXAMPLE APPLICATIONS HBase by example: OpenTSDB Scaling GIS on HBase PART 4 OPERATIONALIZING HBASE Deploying HBase Operations
If you're looking for a scalable storage solution to accommodate a virtually endless amount of data, this book shows you how Apache HBase can fulfill your needs. As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant. Many IT executives are asking pointed questions about HBase. This book provides meaningful answers, whether you’re evaluating this non-relational database or planning to put it into practice right away. Discover how tight integration with Hadoop makes scalability with HBase easier Distribute large datasets across an inexpensive cluster of commodity servers Access HBase with native Java clients, or with gateway servers providing REST, Avro, or Thrift APIs Get details on HBase’s architecture, including the storage format, write-ahead log, background processes, and more Integrate HBase with Hadoop's MapReduce framework for massively parallelized data processing jobs Learn how to tune clusters, design schemas, copy tables, import bulk data, decommission nodes, and many other tasks
HBase data storage technology is rapidly adopted by traditional RDMS users. Unlike RDMS, where scaling the server vertically for a huge data is a big challenge. With HBase, you can do this easily. It allows you to integrate with Hadoop's MapReduce framework for massively parallelized data processing jobs. Many expert and beginners are asking for a point-to-point guide that helps them to get a complete insight on HBase working. This book will answer all their queries and give them a complete tour of HBase technology. In this edition, you will begin with some very basic concept like HBase’s architecture, including the storage format, write-ahead log, background processes, and some of the advance topics. You will also learn about accessing HBase with native Java clients, how to tune clusters, design schemas, copy tables, etc. So far if tracking other resources for HBase have disappointed you, you must try this e-book. It is cheap, easy to comprehend and concise in its content. The examples and images are an additional benefit of this book. While to enhance your knowledge pool for related topics, more referrals and links are provided. Table Of Contents Chapter 1: Introduction Chapter 2: Architecture, Data Flow, and Use cases Storage Mechanism in Hbase HBase Architecture and its Important Components Data flow in HBase HBASE vs. HDFS Chapter 3: Installation Guide How to Download Hbase tar file stable version Hbase - Standalone mode installation Hbase - Pseudo Distributed mode of installation Hbase - Fully Distributed mode installation Chapter 4: Shell and General Commands General commands Tables Managements commands Data manipulation commands Cluster Replication Commands Chapter 5: Handling Tables Creation of Table with Rows and Column names Placing values into tables and retrieving values from table Retrieving Inserted Values in HBase shell mode Chapter 6: Limitations, Advantage & Problems Chapter 7: Troubleshooting
Lots of HBase books, online HBase guides, and HBase mailing lists/forums are available if you need to know how HBase works. But if you want to take a deep dive into use cases, features, and troubleshooting, Architecting HBase Applications is the right source for you. With this book, you'll learn a controlled set of APIs that coincide with use-case examples and easily deployed use-case models, as well as sizing/best practices to help jump start your enterprise application development and deployment.
If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.
As part of Packt's cookbook series, each recipe offers a practical, step-by-step solution to common problems found in HBase administration. This book is for HBase administrators, developers, and will even help Hadoop administrators. You are not required to have HBase experience, but are expected to have a basic understanding of Hadoop and MapReduce.
Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN). Store large datasets with the Hadoop Distributed File System (HDFS) Run distributed computations with MapReduce Use Hadoop’s data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud Load data from relational databases into HDFS, using Sqoop Perform large-scale data processing with the Pig query language Analyze datasets with Hive, Hadoop’s data warehousing system Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems
Perform forensic investigations on Hadoop clusters with cutting-edge tools and techniques About This Book Identify, collect, and analyze Hadoop evidence forensically Learn about Hadoop's internals and Big Data file storage concepts A step-by-step guide to help you perform forensic analysis using freely available tools Who This Book Is For This book is meant for statisticians and forensic analysts with basic knowledge of digital forensics. They do not need to know Big Data Forensics. If you are an IT professional, law enforcement professional, legal professional, or a student interested in Big Data and forensics, this book is the perfect hands-on guide for learning how to conduct Hadoop forensic investigations. Each topic and step in the forensic process is described in accessible language. What You Will Learn Understand Hadoop internals and file storage Collect and analyze Hadoop forensic evidence Perform complex forensic analysis for fraud and other investigations Use state-of-the-art forensic tools Conduct interviews to identify Hadoop evidence Create compelling presentations of your forensic findings Understand how Big Data clusters operate Apply advanced forensic techniques in an investigation, including file carving, statistical analysis, and more In Detail Big Data forensics is an important type of digital investigation that involves the identification, collection, and analysis of large-scale Big Data systems. Hadoop is one of the most popular Big Data solutions, and forensically investigating a Hadoop cluster requires specialized tools and techniques. With the explosion of Big Data, forensic investigators need to be prepared to analyze the petabytes of data stored in Hadoop clusters. Understanding Hadoop's operational structure and performing forensic analysis with court-accepted tools and best practices will help you conduct a successful investigation. Discover how to perform a complete forensic investigation of large-scale Hadoop clusters using the same tools and techniques employed by forensic experts. This book begins by taking you through the process of forensic investigation and the pitfalls to avoid. It will walk you through Hadoop's internals and architecture, and you will discover what types of information Hadoop stores and how to access that data. You will learn to identify Big Data evidence using techniques to survey a live system and interview witnesses. After setting up your own Hadoop system, you will collect evidence using techniques such as forensic imaging and application-based extractions. You will analyze Hadoop evidence using advanced tools and techniques to uncover events and statistical information. Finally, data visualization and evidence presentation techniques are covered to help you properly communicate your findings to any audience. Style and approach This book is a complete guide that follows every step of the forensic analysis process in detail. You will be guided through each key topic and step necessary to perform an investigation. Hands-on exercises are presented throughout the book, and technical reference guides and sample documents are included for real-world use.
Leverage Phoenix as an ANSI SQL engine built on top of the highly distributed and scalable NoSQL framework HBase. Learn the basics and best practices that are being adopted in Phoenix to enable a high write and read throughput in a big data space. This book includes real-world cases such as Internet of Things devices that send continuous streams to Phoenix, and the book explains how key features such as joins, indexes, transactions, and functions help you understand the simple, flexible, and powerful API that Phoenix provides. Examples are provided using real-time data and data-driven businesses that show you how to collect, analyze, and act in seconds. Pro Apache Phoenix covers the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios, and viewing the results. The book also shows how Phoenix plays well with other key frameworks in the Hadoop ecosystem such as Apache Spark, Pig, Flume, and Sqoop. You will learn how to: Handle a petabyte data store by applying familiar SQL techniques Store, analyze, and manipulate data in a NoSQL Hadoop echo system with HBase Apply best practices while working with a scalable data store on Hadoop and HBase Integrate popular frameworks (Apache Spark, Pig, Flume) to simplify big data analysis Demonstrate real-time use cases and big data modeling techniques Who This Book Is For Data engineers, Big Data administrators, and architects.