Annotation IT Managers, developers, data analysts, system architects, and similar technical workers are now encountering the largest and most disruptive change in their profession since the ascendancy of the relational database in early 1980s. You hear that NoSQL and Big Data Analytics are about to replace the systems and skills you now own and possess, but there's often no easy way to make that transition. To exacerbate the issue, the transition may not be gradual, but forced on you by a new project in your enterprisenamely, Hadoopthat will immediately require new ways of thinking, new tools, and new techniques. This book helps you understand the components of the Hadoop ecosystem and how they relate to each other. You'll discover how to get started on that project in an efficient manner that lays out the possibilities. The authors suggest a path and resources that will guide you on their journey from the status quo to the Brave New World you face.

The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.

Covers SharePoint 2013, Office 365’s SharePoint Online, and Other Office 365 Components In SharePoint 2013 Field Guide, top consultant Errin O’Connor and the team from EPC Group bring together best practices and proven strategies drawn from hundreds of successful SharePoint and Office 365 engagements. Reflecting this unsurpassed experience, they guide you through deployments of every type, including the latest considerations around private, public, and hybrid cloud implementations, from ECM to business intelligence (BI), as well as custom development and identity management. O’Connor reveals how world-class consultants approach, plan, implement, and deploy SharePoint 2013 and Office 365’s SharePoint Online to maximize both short- and long-term value. He covers every phase and element of the process, including initial “whiteboarding”; consideration around the existing infrastructure; IT roadmaps and the information architecture (IA); and planning for security and compliance in the new IT landscape of the hybrid cloud. SharePoint 2013 Field Guide will be invaluable for implementation team members ranging from solution architects to support professionals, CIOs to end-users. It’s like having a team of senior-level SharePoint and Office 365 hybrid architectureconsultants by your side, helping you optimize your success from start to finish! Detailed Information on How to… Develop a 24-36 month roadmap reflecting initial requirements, longterm strategies, and key unknowns for organizations from 100 users to 100,000 users Establish governance that reduces risk and increases value, covering the system as well as information architecture components, security, compliance, OneDrive, SharePoint 2013, Office 365, SharePoint Online, Microsoft Azure, Amazon Web Services, and identity management Address unique considerations of large, global, and/or multilingual enterprises Plan for the hybrid cloud (private, public, hybrid, SaaS, PaaS, IaaS) Integrate SharePoint with external data sources: from Oracle and SQL Server to HR, ERP, or document management for business intelligence initiatives Optimize performance across multiple data centers or locations including US and EU compliance and regulatory considerations (PHI, PII, HIPAA, Safe Harbor, etc.) Plan for disaster recovery, business continuity, data replication, and archiving Enforce security via identity management and authentication Safely support mobile devices and apps, including BYOD Implement true records management (ECM/RM) to support legal/compliance requirements Efficiently build custom applications, workflows, apps and web parts Leverage Microsoft Azure or Amazon Web Services (AWS)

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

With the latest edition of this comprehensive resource, readers will learn how to use Apache Hadoop to build and maintain reliable, scalable, distributed systems. Ideal for programmers and administrators wanting to set up and analyze datasets of any size.

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

Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, youâ??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. Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. Youâ??ll learn about recent changes to Hadoop, and explore new case studies on Hadoopâ??s role in healthcare systems and genomics data processing. Learn fundamental components such as MapReduce, HDFS, and YARN Explore MapReduce in depth, including steps for developing applications with it Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN Learn two data formats: Avro for data serialization and Parquet for nested data Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer) Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop Learn the HBase distributed database and the ZooKeeper distributed configuration service

There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies, Practical Hive gives you a detailed treatment of the software. In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data. What You Will Learn Install and configure Hive for new and existing datasets Perform DDL operations Execute efficient DML operations Use tables, partitions, buckets, and user-defined functions Discover performance tuning tips and Hive best practices Who This Book Is For Developers, companies, and professionals who deal with large amounts of data and could use software that can efficiently manage large volumes of input. It is assumed that readers have the ability to work with SQL.

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills. "Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems. Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems. While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection. In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.