Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Un ouvrage destiné aux décideurs, qu'ils soient techniques ou pas, pour démystifier le big data et Hadoop en mettant l'accent sur leur utilisation et leur impact potentiels.

Big data. --- Données volumineuses --- Apache Hadoop

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is a practical guide on using 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 is a practical tutorial on using a 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 these all work together as a cohesive big data development platform. What you'll learn How to set up environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5. How to run a MapReduce job How to store data with Apache Hive, Apache HBase How to index data in HDFS with Apache Solr How to develop a Kafka messaging system How to develop a Mahout User Recommender System How to stream Logs to HDFS with Apache Flume How to transfer data from MySQL database to Hive, HDFS and HBase with Sqoop How create a Hive table over Apache Solr.

Computer science --- Information systems --- MySQL --- Apache (informatica) --- big data --- computers --- database management --- gegevensanalyse --- computerkunde --- Big data. --- Database management. --- Big Data. --- Database Management. --- Apache Hadoop.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Computer Science -- IT. --- Expert systems (Computer science). --- Software engineering. --- Computer Science --- Engineering & Applied Sciences --- Information Technology --- Computer Science (Hardware & Networks) --- General and Others --- Computer software. --- Hadoop. --- Software, Computer --- Hadoop --- Computer science. --- Computer Science. --- Computer Science, general. --- Informatics --- Science --- Computer systems --- Big data. --- Big Data. --- Software Engineering/Programming and Operating Systems. --- Computer software engineering --- Engineering --- Data sets, Large --- Large data sets --- Data sets

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Pro Microsoft HDInsight is a complete guide to deploying and using Apache Hadoop on the Microsoft Windows Azure Platforms. The information in this book enables you to process enormous volumes of structured as well as non-structured data easily using HDInsight, which is Microsoft’s own distribution of Apache Hadoop. Furthermore, the blend of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings available through Windows Azure lets you take advantage of Hadoop’s processing power without the worry of creating, configuring, maintaining, or managing your own cluster. With the data explosion that is soon to happen, the open source Apache Hadoop Framework is gaining traction, and it benefits from a huge ecosystem that has risen around the core functionalities of the Hadoop distributed file system (HDFS™) and Hadoop Map Reduce. Pro Microsoft HDInsight equips you with the knowledge, confidence, and technique to configure and manage this ecosystem on Windows Azure. The book is an excellent choice for anyone aspiring to be a data scientist or data engineer, putting you a step ahead in the data mining field. Guides you through installation and configuration of an HDInsight cluster on Windows Azure Provides clear examples of configuring and executing Map Reduce jobs Helps you consume data and diagnose errors from the Windows Azure HDInsight Service.

Computer science --- Information systems --- Apache (informatica) --- computers --- database management --- computerkunde --- data acquisition --- Microsoft software. --- Microsoft .NET Framework. --- Data mining. --- Microsoft and .NET. --- Data Mining and Knowledge Discovery. --- Apache Hadoop.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Re-architect relational applications to NoSQL, integrate relational database management systems with the Hadoop ecosystem, and transform and migrate relational data to and from Hadoop components. This book covers the best-practice design approaches to re-architecting your relational applications and transforming your relational data to optimize concurrency, security, denormalization, and performance. Winner of IBM’s 2012 Gerstner Award for his implementation of big data and data warehouse initiatives and author of Practical Hadoop Security, author Bhushan Lakhe walks you through the entire transition process. First, he lays out the criteria for deciding what blend of re-architecting, migration, and integration between RDBMS and HDFS best meets your transition objectives. Then he demonstrates how to design your transition model. Lakhe proceeds to cover the selection criteria for ETL tools, the implementation steps for migration with SQOOP- and Flume-based data transfers, and transition optimization techniques for tuning partitions, scheduling aggregations, and redesigning ETL. Finally, he assesses the pros and cons of data lakes and Lambda architecture as integrative solutions and illustrates their implementation with real-world case studies. Hadoop/NoSQL solutions do not offer by default certain relational technology features such as role-based access control, locking for concurrent updates, and various tools for measuring and enhancing performance. Practical Hadoop Migration shows how to use open-source tools to emulate such relational functionalities in Hadoop ecosystem components. What You'll Learn Decide whether you should migrate your relational applications to big data technologies or integrate them Transition your relational applications to Hadoop/NoSQL platforms in terms of logical design and physical implementation Discover RDBMS-to-HDFS integration, data transformation, and optimization techniques Consider when to use Lambda architecture and data lake solutions Select and implement Hadoop-based components and applications to speed transition, optimize integrated performance, and emulate relational functionalities Who This Book Is For Database developers, database administrators, enterprise architects, Hadoop/NoSQL developers, and IT leaders. Its secondary readership is project and program managers and advanced students of database and management information systems.

Logic --- Computer science --- Information systems --- Computer. Automation --- big data --- computers --- informatica --- ontwerpen --- database management --- programmatielogica --- computerkunde --- Database management. --- Computer science. --- Data structures (Computer science) --- Logic design. --- Database Management. --- Computer Science, general. --- Data Structures. --- Logic Design. --- Apache Hadoop.