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Hadoop has changed the way large data sets are analyzed, stored, transferred, and processed. At such low cost, it provides benefits like supports partial failure, fault tolerance, consistency, scalability, flexible schema, and so on. It also supports cloud computing. More and more number of individuals are looking forward to mastering their Hadoop skills. While initiating with Hadoop, most users are unsure about how to proceed with Hadoop. They are not aware of what are the pre-requisite or data structure they should be familiar with. Or How to make the most efficient use of Hadoop and its ecosystem. To help them with all these queries and other issues this e-book is designed. The book gives insights into many of Hadoop libraries and packages that are not known to many Big data Analysts and Architects. The e-book also tells you about Hadoop MapReduce and HDFS. The example in the e-book is well chosen and demonstrates how to control Hadoop ecosystem through various shell commands. With this book, users will gain expertise in Hadoop technology and its related components. The book leverages you with the best Hadoop content with the lowest price range. After going through this book, you will also acquire knowledge on Hadoop Security required for Hadoop Certifications like CCAH and CCDH. It is a definite guide to Hadoop. Table Contents Chapter 1: What Is Big Data Examples Of 'Big Data' Categories Of 'Big Data' Characteristics Of 'Big Data' Advantages Of Big Data Processing Chapter 2: Introduction to Hadoop Components of Hadoop Features Of 'Hadoop' Network Topology In Hadoop Chapter 3: Hadoop Installation Chapter 4: HDFS Read Operation Write Operation Access HDFS using JAVA API Access HDFS Using COMMAND-LINE INTERFACE Chapter 5: Mapreduce How MapReduce works How MapReduce Organizes Work? Chapter 6: First Program Understanding MapReducer Code Explanation of SalesMapper Class Explanation of SalesCountryReducer Class Explanation of SalesCountryDriver Class Chapter 7: Counters & Joins In MapReduce Two types of counters MapReduce Join Chapter 8: MapReduce Hadoop Program To Join Data Chapter 9: Flume and Sqoop What is SQOOP in Hadoop? What is FLUME in Hadoop? Some Important features of FLUME Chapter 10: Pig Introduction to PIG Create your First PIG Program PART 1) Pig Installation PART 2) Pig Demo Chapter 11: OOZIE What is OOZIE? How does OOZIE work? Example Workflow Diagram Oozie workflow application Why use Oozie? FEATURES OF OOZIE
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
If you’ve been asked to maintain large and complex Hadoop clusters, this book is a must. Demand for operations-specific material has skyrocketed now that Hadoop is becoming the de facto standard for truly large-scale data processing in the data center. Eric Sammer, Principal Solution Architect at Cloudera, shows you the particulars of running Hadoop in production, from planning, installing, and configuring the system to providing ongoing maintenance. Rather than run through all possible scenarios, this pragmatic operations guide calls out what works, as demonstrated in critical deployments. Get a high-level overview of HDFS and MapReduce: why they exist and how they work Plan a Hadoop deployment, from hardware and OS selection to network requirements Learn setup and configuration details with a list of critical properties Manage resources by sharing a cluster across multiple groups Get a runbook of the most common cluster maintenance tasks Monitor Hadoop clusters—and learn troubleshooting with the help of real-world war stories Use basic tools and techniques to handle backup and catastrophic failure
Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs. The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework. This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples. 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.
Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout, and Apache Solr. From setting up the environment to running sample applications each chapter in this book is a practical tutorial on using an Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects, MapReduce and HDFS, and none discusses the other Apache Hadoop ecosystem projects and how they all work together as a cohesive big data development platform. What You Will Learn: Set up the environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5 Run a MapReduce job Store data with Apache Hive, and Apache HBase Index data in HDFS with Apache Solr Develop a Kafka messaging system Stream Logs to HDFS with Apache Flume Transfer data from MySQL database to Hive, HDFS, and HBase with Sqoop Create a Hive table over Apache Solr Develop a Mahout User Recommender System Who This Book Is For: Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.
CodeIgniter is a MVC (Model View Controller) framework for developing PHP applications quickly. It provides out of the box libraries for connecting to the database and performing various operations. Here is what is covered in the book – Chapter 1: What is CodeIgniter? How does it Work? 1. What is CodeIgniter? 2. CodeIgniter Features 3. How CodeIgniter Works? 4. CodeIgniter Release History Chapter 2: How to Download & Install CodeIgniter + Composer [Configuration Included] 1. Download and Install Latest CodeIgniter Framework 2. What is Composer? 3. How to install Composer 4. CodeIgniter Config Files 5. CodeIgniter Configurations 6. How to remove index.php in CodeIgniter Chapter 3: CodeIgniter Application's FOLDER & FILE Structure 1. Application subdirectories 2. System subdirectories 3. User_guide directory 4. Vendor directory Chapter 4: CodeIgniter MVC(Model View Controller) Framework with Example 1. What is MVC? 2. How MVC frameworks work? 3. CodeIgniter Controller 4. CodeIgniter Model Chapter 5: CodeIgniter Controllers, Views Routing: Learn with Example App 1. How to create a new CodeIgniter project 2. CodeIgniter Routing 3. Create a Route 4. Create a Controller 5. Create a View Chapter 6: CodeIgniter Routes: Learn with Example 1. What are Routes? 2. Routes Example 3. Creating URL's for the Application 4. Views Chapter 7: CodeIgniter Form & Form Validation with Example 1. CodeIgniter Form Helper 2. Example Create Form 3. CodeIgniter Form Validation 4. Adding Form Validation Rules 5. Displaying Form Validation Error Messages 6. Populating Submitted Form Data: Sticky Forms 7. Example Form Validation Chapter 8: Codeigniter Active Record: Insert, Select, Update, Delete 1. How to use Active Record: Example 2. CodeIgniter Database Configuration 3. CodeIgniter Insert Active Record 4. CodeIgniter Select Active Record 5. CodeIgniter Update Active Record 6. CodeIgniter Delete Active Record Chapter 9: CodeIgniter Database Tutorial: Create, Update, Delete 1. CodeIgniter Working with Database 2. Database Configuration 3. CodeIgniter Database Models 4. Contacts Manager Views Chapter 10: Pagination in Codeigniter with Step by Step Example 1. Database configuration 2. CodeIgniter Pagination Database Model 3. CodeIgniter Pagination Routes 4. CodeIgniter Pagination Controller Chapter 11: How to Set Session in Codeigniter With Example 1. CodeIgniter Session Management 2. When to use sessions? 3. Sending Flash Messages to other pages with CI Sessions 4. Storing User Data in CI Sessions 5. CodeIgniter Session Views Chapter 12: How to Upload Image & File in CodeIgniter (with Example) 1. CodeIgniter File Upload 2. Uploading Images in CodeIgniter 3. Testing the application Chapter 13: How to Send Email using CodeIgniter 1. CodeIgniter Email Configuration 2. CodeIgniter Email View 3. CodeIgniter Email Controller 4. Email Routes Chapter 14: Laravel vs CodeIgniter: Which is Better? 1. What is Laravel? 2. What is CodeIgniter? 3. Why use Laravel? 4. Why use CodeIgniter? 5. Features of Laravel 6. Features of CodeIgniter 7. Laravel vs. CodeIgniter: Know the Difference 8. Laravel vs. CodeIgniter which is better? Click the BUY button now and download the book now to start learning UML. Learn it fast and learn it well. Pick up your copy today by clicking the BUY NOW button at the top of this page!
You've heard the hype about Hadoop: it runs petabyte–scale data mining tasks insanely fast, it runs gigantic tasks on clouds for absurdly cheap, it's been heavily committed to by tech giants like IBM, Yahoo!, and the Apache Project, and it's completely open-source (thus free). But what exactly is it, and more importantly, how do you even get a Hadoop cluster up and running? From Apress, the name you've come to trust for hands–on technical knowledge, Pro Hadoop brings you up to speed on Hadoop. You learn the ins and outs of MapReduce; how to structure a cluster, design, and implement the Hadoop file system; and how to build your first cloud–computing tasks using Hadoop. Learn how to let Hadoop take care of distributing and parallelizing your software—you just focus on the code, Hadoop takes care of the rest. Best of all, you'll learn from a tech professional who's been in the Hadoop scene since day one. Written from the perspective of a principal engineer with down–in–the–trenches knowledge of what to do wrong with Hadoop, you learn how to avoid the common, expensive first errors that everyone makes with creating their own Hadoop system or inheriting someone else's. Skip the novice stage and the expensive, hard–to–fix mistakes...go straight to seasoned pro on the hottest cloud–computing framework with Pro Hadoop. Your productivity will blow your managers away.
A comprehensive guide to mastering the most advanced Hadoop 3 concepts Key FeaturesGet to grips with the newly introduced features and capabilities of Hadoop 3Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystemSharpen your Hadoop skills with real-world case studies and codeBook Description 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. What you will learnGain an in-depth understanding of distributed computing using Hadoop 3Develop enterprise-grade applications using Apache Spark, Flink, and moreBuild scalable and high-performance Hadoop data pipelines with security, monitoring, and data governanceExplore batch data processing patterns and how to model data in HadoopMaster best practices for enterprises using, or planning to use, Hadoop 3 as a data platformUnderstand security aspects of Hadoop, including authorization and authenticationWho this book is for If you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You’ll also find this book useful if you’re a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.
Summary Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available. Readers need to know a programming language like Java and have basic familiarity with Hadoop. What's Inside Thoroughly updated for Hadoop 2 How to write YARN applications Integrate real-time technologies like Storm, Impala, and Spark Predictive analytics using Mahout and RR Readers need to know a programming language like Java and have basic familiarity with Hadoop. About the Author Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects. Table of Contents PART 1 BACKGROUND AND FUNDAMENTALS Hadoop in a heartbeat Introduction to YARN PART 2 DATA LOGISTICS Data serialization—working with text and beyond Organizing and optimizing data in HDFS Moving data into and out of Hadoop PART 3 BIG DATA PATTERNS Applying MapReduce patterns to big data Utilizing data structures and algorithms at scale Tuning, debugging, and testing PART 4 BEYOND MAPREDUCE SQL on Hadoop Writing a YARN application
Hadoop has changed the way large data sets are analyzed, stored, transferred, and processed. At such low cost, it provides benefits like supports partial failure, fault tolerance, consistency, scalability, flexible schema, and so on. It also supports cloud computing. More and more number of individuals are looking forward to mastering their Hadoop skills. While initiating with Hadoop, most users are unsure about how to proceed with Hadoop. They are not aware of what are the pre-requisite or data structure they should be familiar with. Or How to make the most efficient use of Hadoop and its ecosystem. To help them with all these queries and other issues this e-book is designed. The book gives insights into many of Hadoop libraries and packages that are not known to many Big data Analysts and Architects. The e-book also tells you about Hadoop MapReduce and HDFS. The example in the e-book is well chosen and demonstrates how to control Hadoop ecosystem through various shell commands. With this book, users will gain expertise in Hadoop technology and its related components. The book leverages you with the best Hadoop content with the lowest price range. After going through this book, you will also acquire knowledge on Hadoop Security required for Hadoop Certifications like CCAH and CCDH. It is a definite guide to Hadoop. Table Of Content Chapter 1: What Is Big Data 1. Examples Of 'Big Data' 2. Categories Of 'Big Data' 3. Characteristics Of 'Big Data' 4. Advantages Of Big Data Processing Chapter 2: Introduction to Hadoop 1. Components of Hadoop 2. Features Of 'Hadoop' 3. Network Topology In Hadoop Chapter 3: Hadoop Installation Chapter 4: HDFS 1. Read Operation 2. Write Operation 3. Access HDFS using JAVA API 4. Access HDFS Using COMMAND-LINE INTERFACE Chapter 5: Mapreduce 1. How MapReduce works 2. How MapReduce Organizes Work? Chapter 6: First Program 1. Understanding MapReducer Code 2. Explanation of SalesMapper Class 3. Explanation of SalesCountryReducer Class 4. Explanation of SalesCountryDriver Class Chapter 7: Counters & Joins In MapReduce 1. Two types of counters 2. MapReduce Join Chapter 8: MapReduce Hadoop Program To Join Data Chapter 9: Flume and Sqoop 1. What is SQOOP in Hadoop? 2. What is FLUME in Hadoop? 3. Some Important features of FLUME Chapter 10: Pig 1. Introduction to PIG 2. Create your First PIG Program 3. PART 1) Pig Installation 4. PART 2) Pig Demo Chapter 11: OOZIE 1. What is OOZIE? 2. How does OOZIE work? 3. Example Workflow Diagram 4. Oozie workflow application 5. Why use Oozie? 6. FEATURES OF OOZIE