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Field Guide to Hadoop by Kevin Sitto,Marshall Presser Pdf
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.
Field Guide to Hadoop by Kevin Sitto,Marshall Presser Pdf
If your organization is about to enter the world of big data, you not only need to decide whether Apache Hadoop is the right platform to use, but also which of its many components are best suited to your task. This field guide makes the exercise manageable by breaking down the Hadoop ecosystem into short, digestible sections. You’ll quickly understand how Hadoop’s projects, subprojects, and related technologies work together. Each chapter introduces a different topic—such as core technologies or data transfer—and explains why certain components may or may not be useful for particular needs. When it comes to data, Hadoop is a whole new ballgame, but with this handy reference, you’ll have a good grasp of the playing field. Topics include: Core technologies—Hadoop Distributed File System (HDFS), MapReduce, YARN, and Spark Database and data management—Cassandra, HBase, MongoDB, and Hive Serialization—Avro, JSON, and Parquet Management and monitoring—Puppet, Chef, Zookeeper, and Oozie Analytic helpers—Pig, Mahout, and MLLib Data transfer—Scoop, Flume, distcp, and Storm Security, access control, auditing—Sentry, Kerberos, and Knox Cloud computing and virtualization—Serengeti, Docker, and Whirr
If your organization is about to enter the world of big data, you not only need to decide whether Apache Hadoop is the right platform to use, but also which of its many components are best suited to your task. This field guide makes the exercise manageable by breaking down the Hadoop ecosystem into short, digestible sections. You{u2019}ll quickly understand how Hadoop{u2019}s projects, subprojects, and related technologies work together. Each chapter introduces a different topic{u2014}such as core technologies or data transfer{u2014}and explains why certain components may or may not be useful for particular needs. When it comes to data, Hadoop is a whole new ballgame, but with this handy reference, you{u2019}ll have a good grasp of the playing field. Topics include: Core technologies{u2014}Hadoop Distributed File System (HDFS), MapReduce, YARN, and Spark Database and data management{u2014}Cassandra, HBase, MongoDB, and Hive Serialization{u2014}Avro, JSON, and Parquet Management and monitoring{u2014}Puppet, Chef, Zookeeper, and Oozie Analytic helpers{u2014}Pig, Mahout, and MLLib Data transfer{u2014}Scoop, Flume, distcp, and Storm Security, access control, auditing{u2014}Sentry, Kerberos, and Knox Cloud computing and virtualization{u2014}Serengeti, Docker, and Whirr.
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
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
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)
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.
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.
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
Real-World Hadoop by Ted Dunning,Ellen Friedman Pdf
If you’re a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues. You’ll learn about early decisions and pre-planning that can make the process easier and more productive. If you’re already using these technologies, you’ll discover ways to gain the full range of benefits possible with Hadoop. While you don’t need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects. Examine a day in the life of big data: India’s ambitious Aadhaar project Review tools in the Hadoop ecosystem such as Apache’s Spark, Storm, and Drill to learn how they can help you Pick up a collection of technical and strategic tips that have helped others succeed with Hadoop Learn from several prototypical Hadoop use cases, based on how organizations have actually applied the technology Explore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production
Emerging Research in Computing, Information, Communication and Applications by N. R. Shetty,L. M. Patnaik,H. C. Nagaraj,Prasad Naik Hamsavath,N. Nalini Pdf
This book presents selected papers from the International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018. The conference provided an interdisciplinary forum for researchers, professional engineers and scientists, educators, and technologists to discuss, debate and promote research and technology in the emerging areas of computing, information, communication and their applications. The book discusses these research areas, providing a valuable resource for researchers and practicing engineers alike.
Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system. As Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset shows, Apache Hadoop offers a scalable, fault-tolerant system for storing and processing data in parallel. It has a very rich toolset that allows for storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive). The problem is that the Internet offers IT pros wading into big data many versions of the truth and some outright falsehoods born of ignorance. What is needed is a book just like this one: a wide-ranging but easily understood set of instructions to explain where to get Hadoop tools, what they can do, how to install them, how to configure them, how to integrate them, and how to use them successfully. And you need an expert who has worked in this area for a decade—someone just like author and big data expert Mike Frampton. Big Data Made Easy approaches the problem of managing massive data sets from a systems perspective, and it explains the roles for each project (like architect and tester, for example) and shows how the Hadoop toolset can be used at each system stage. It explains, in an easily understood manner and through numerous examples, how to use each tool. The book also explains the sliding scale of tools available depending upon data size and when and how to use them. Big Data Made Easy shows developers and architects, as well as testers and project managers, how to: Store big data Configure big data Process big data Schedule processes Move data among SQL and NoSQL systems Monitor data Perform big data analytics Report on big data processes and projects Test big data systems Big Data Made Easy also explains the best part, which is that this toolset is free. Anyone can download it and—with the help of this book—start to use it within a day. With the skills this book will teach you under your belt, you will add value to your company or client immediately, not to mention your career.
Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters Exploring the Hadoop Distributed File System (HDFS) Understanding the essentials of MapReduce and YARN application programming Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase Observing application progress, controlling jobs, and managing workflows Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark
Intelligent Information and Database Systems by Ngoc Thanh Nguyen,Duong Hung Hoang,Tzung-Pei Hong,Hoang Pham,Bogdan Trawiński Pdf
The two-volume set LNAI 10751 and 10752 constitutes the refereed proceedings of the 10th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2018, held in Dong Hoi City, Vietnam, in March 2018. The total of 133 full papers accepted for publication in these proceedings was carefully reviewed and selected from 423 submissions. They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Machine Learning and Data Mining; Decision Support and Control Systems; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Multiple Model Approach to Machine Learning; Sensor Networks and Internet of Things; Intelligent Information Systems; Data Structures Modeling for Knowledge Representation; Modeling, Storing, and Querying of Graph Data; Data Science and Computational Intelligence; Design Thinking Based R&D, Development Technique, and Project Based Learning; Intelligent and Contextual Systems; Intelligent Systems and Algorithms in Information Sciences; Intelligent Applications of Internet of Thing and Data Analysis Technologies; Intelligent Systems and Methods in Biomedicine; Intelligent Biomarkers of Neurodegenerative Processes in Brain; Analysis of Image, Video and Motion Data in Life Sciences; Computational Imaging and Vision; Computer Vision and Robotics; Intelligent Computer Vision Systems and Applications; Intelligent Systems for Optimization of Logistics and Industrial Applications.
Architecting Modern Data Platforms by Jan Kunigk,Ian Buss,Paul Wilkinson,Lars George Pdf
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
