Storing and Managing Big Data - NoSQL, Hadoop and More: High-impact Strategies - What You Need to Know

Storing and Managing Big Data - NoSQL, Hadoop and More: High-impact Strategies - What You Need to Know
Author: Kevin Roebuck
Publisher: Tebbo
Total Pages: 0
Release: 2011
Genre: Computers
ISBN: 9781743045749
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Over the last several years there are two important trends that require additional thought when putting together an architecture for a hosted service. The ability to analyze and process enormous amounts of data is increasingly important. From a technology perspective, the two trends to focus on are: 1. Batch processing -- the increasing awareness of batch processing and the recent uptick in use of the map educe paradigm for that purpose; Distributed computing is a field of computer science that studies distributed systems. 2. NoSQL stores - The rise of so called ""NoSQL"" stores and their use to serve up data to online users; a distributed file system or network file system is any file system that allows access to files from multiple hosts sharing via a computer network. This makes it possible for multiple users on multiple machines to share files and storage resources. Both of these trends represent significant advances in the way that hosted systems are developed. But in order to derive the most value for an entire system, developers must think about how these two areas will work together in some holistic manner. This book is your ultimate resource for Storing and managing big data- NoSQL, Hadoop and more. Here you will find the most up-to-date information, analysis, background and everything you need to know. In easy to read chapters, with extensive references and links to get you to know all there is to know about Storing and managing big data- NoSQL, Hadoop and more right away, covering: Distributed data store, Background Intelligent Transfer Service, BATON Overlay, BitVault, Bootstrapping node, Chimera (software library), Chord (peer-to-peer), Cloud (operating system), CoDeeN, Collaber, Collanos, Comparison of streaming media systems, Comparison of video hosting services, Content addressable network, Content delivery network, Coral Content Distribution Network, Data center, Distributed file system, Distributed hash table, Distributed Networking, FAROO, Globule (CDN), GlusterFS, Grid casting, Hibari (database), High performance cloud computing, HTTP(P2P), Hyper distribution, Infrastructure for Resilient Internet Systems, Jigdo, JXTA, Kademlia, Key-based routing, Koorde, Legion (software), MagmaFS, Metalink, NeoEdge Networks, Octoshape, Ono (P2P), Osiris (Serverless Portal System), OverSim, P-Grid, P2P-Next, P2PTV, PAST storage utility, Pastry (DHT), Peer-to-peer wiki, Prefix hash tree, Proactive network Provider Participation for P2P, Rawflow, Sciencenet, Similarity Enhanced Transfer, Space-based architecture, Superdistribution, Tapestry (DHT), Tulip Overlay, Tuotu, Web acceleration, YaCy, Aquiles, BigTable, Apache Cassandra, Column family, Hector (API), Keyspace (distributed data store), NoSQL, Standard column family, Super column family, Tombstone (data store), Voldemort (distributed data store), Andrew File System, Apache Hadoop, Apache Hive, BigCouch, Ceph, The Circle (file system), Cloudant, Cloudera, CloudStore, DCE Distributed File System, Direct Access File System, Distributed File System (Microsoft), FhGFS, Gfarm file system, Global Storage Architecture, Google File System, HAMMER, IBM General Parallel File System, Infinit, Lustre (file system), MapR, Moose File System, OFFSystem, OneFS distributed file system, Parallel Virtual File System, POHMELFS, Sector/Sphere, Storage@home, Tahoe Least-Authority Filesystem, Wuala, XtreemFS This book explains in-depth the real drivers and workings of Storing and managing big data- NoSQL, Hadoop and more. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Storing and managing big data- NoSQL, Hadoop and more with the objectivity of experienced professionals.

Managing Big Data

Managing Big Data
Author: Chandrakant Naikodi
Publisher: Vikas Publishing House
Total Pages:
Release:
Genre: Computers
ISBN: 9325984563
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Managing Big Data is a simple book which introduces students and professionals to Big Data. Although the book has been designed for unassisted reading, lot of insights from the author makes this a very thoughtful book which will automatically lead to yearning for more learning on the subject.

Making Sense of NoSQL

Making Sense of NoSQL
Author: Ann Kelly
Publisher: Simon and Schuster
Total Pages: 459
Release: 2013-09-02
Genre: Computers
ISBN: 1638351422
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Summary Making Sense of NoSQL clearly and concisely explains the concepts, features, benefits, potential, and limitations of NoSQL technologies. Using examples and use cases, illustrations, and plain, jargon-free writing, this guide shows how you can effectively assemble a NoSQL solution to replace or augment the traditional RDBMS you have now. About this Book If you want to understand and perhaps start using the new data storage and analysis technologies that go beyond the SQL database model, this book is for you. Written in plain language suitable for technical managers and developers, and using many examples, use cases, and illustrations, this book explains the concepts, features, benefits, potential, and limitations of NoSQL. Making Sense of NoSQL starts by comparing familiar database concepts to the new NoSQL patterns that augment or replace them. Then, you'll explore case studies on big data, search, reliability, and business agility that apply these new patterns to today's business problems. You'll see how NoSQL systems can leverage the resources of modern cloud computing and multiple-CPU data centers. The final chaptersshow you how to choose the right NoSQL technologies for your own needs. Managers and developers will welcome this lucid overview of the potential and capabilities of NoSQL technologies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside NoSQL data architecture patterns NoSQL for big data Search, high availability, and security Choosing an architecture About the Authors Dan McCreary and Ann Kelly lead an independent training and consultancy firm focused on NoSQL solutions and are cofounders of the NoSQL Now! Conference. Table of Contents PART 1 INTRODUCTION NoSQL: It's about making intelligent choices NoSQL concepts PART 2 DATABASE PATTERNS Foundational data architecture patterns NoSQL data architecture patterns Native XML databases PART 3 NOSQL SOLUTIONS Using NoSQL to manage big data Finding information with NoSQL search Building high-availability solutions with NoSQL Increasing agility with NoSQL PART 4 ADVANCED TOPICS NoSQL and functional programming Security: protecting data in your NoSQL systems Selecting the right NoSQL solution

Practical Enterprise Data Lake Insights

Practical Enterprise Data Lake Insights
Author: Saurabh Gupta
Publisher: Apress
Total Pages: 335
Release: 2018-07-29
Genre: Computers
ISBN: 1484235223
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Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more. Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point. What You'll Learn Get to know data lake architecture and design principles Implement data capture and streaming strategies Implement data processing strategies in Hadoop Understand the data lake security framework and availability model Who This Book Is For Big data architects and solution architects

Mastering Hadoop 3

Mastering Hadoop 3
Author: Chanchal Singh
Publisher: Packt Publishing Ltd
Total Pages: 531
Release: 2019-02-28
Genre: Computers
ISBN: 1788628322
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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.

Big Data Made Easy

Big Data Made Easy
Author: Michael Frampton
Publisher: Apress
Total Pages: 375
Release: 2015-05-05
Genre: Computers
ISBN: 9781484200964
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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.

NoSQL For Dummies

NoSQL For Dummies
Author: Adam Fowler
Publisher: John Wiley & Sons
Total Pages: 456
Release: 2015-02-24
Genre: Computers
ISBN: 1118905741
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Get up to speed on the nuances of NoSQL databases and what they mean for your organization This easy to read guide to NoSQL databases provides the type of no-nonsense overview and analysis that you need to learn, including what NoSQL is and which database is right for you. Featuring specific evaluation criteria for NoSQL databases, along with a look into the pros and cons of the most popular options, NoSQL For Dummies provides the fastest and easiest way to dive into the details of this incredible technology. You'll gain an understanding of how to use NoSQL databases for mission-critical enterprise architectures and projects, and real-world examples reinforce the primary points to create an action-oriented resource for IT pros. If you're planning a big data project or platform, you probably already know you need to select a NoSQL database to complete your architecture. But with options flooding the market and updates and add-ons coming at a rapid pace, determining what you require now, and in the future, can be a tall task. This is where NoSQL For Dummies comes in! Learn the basic tenets of NoSQL databases and why they have come to the forefront as data has outpaced the capabilities of relational databases Discover major players among NoSQL databases, including Cassandra, MongoDB, MarkLogic, Neo4J, and others Get an in-depth look at the benefits and disadvantages of the wide variety of NoSQL database options Explore the needs of your organization as they relate to the capabilities of specific NoSQL databases Big data and Hadoop get all the attention, but when it comes down to it, NoSQL databases are the engines that power many big data analytics initiatives. With NoSQL For Dummies, you'll go beyond relational databases to ramp up your enterprise's data architecture in no time.

Managing Unstructured Data: NoSQL Database Essentials

Managing Unstructured Data: NoSQL Database Essentials
Author: Anooja Ali
Publisher: MileStone Research Publications
Total Pages: 219
Release: 2024-09-12
Genre: Computers
ISBN: 9334113383
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Managing Unstructured Data: NoSQL Database Essentials-is a reference book and guide for teaching and reading skills to college faculty and students. In Chapter1 the fundamentals of database and relational data base are discussed. This chapter helps students to understand data management concepts by data modelling, schema design, data storage and retrieval. This chapter includes the foundational skills that are applicable across various industries and provides a stepping stone for further specialization and career development. The chapter 2 is all about unstructured data. Varying methods for managing, analysing, and storing data are needed for varying levels of organization and complexity, which are represented by structured, unstructured, and semi-structured data. This chapter provides a platform for students to understand the transition from structured to unstructured data in terms of data management and analysis and it is a pivotal aspect of modern data management. In chapter 3 concepts of NoSQL data base and the major differences with SQL & Relational data bases are highlighted. This chapter explains the adoptions of NoSQL with flexible schema, scalability, high performance and support for distributed architecture. Chapter 4 is all about NoSQL databases, or "Not Only SQL" databases which represent a diverse set of database technologies designed to address specific challenges not well served by traditional relational databases. A brief overview of the main types of NoSQL databases are discussed here. The four basic data models such as key-value pairs, document-oriented, columnar, and graph-based structures are represented in this chapter. Information on popular NoSQL database technologies is given in chapter 5. Details of technologies like Apache HBase, Apache CouchDB, Neo4j, Apache Cassandra and their comparison are also provided here. It includes the distributed architecture with fault tolerance, high availability, and disaster recovery capabilities for ensuring data integrity and business continuity. Chapter 6 discusses the overview of Mongo DB which is a document-oriented NoSQL database known for its flexibility, scalability, and ease of use. The features of Mongo DB including document store, MongoDB protocol, horizontal scalability, cross platform compatibility, replication and sharding are also covered here. Chapter 7 deals with Concurrency control in databases. It discusses about the methods to obtain concurrency in structured data, and then in unstructured data, challenges in concurrency control for unstructured data, commits in transaction and the different isolation levels. Chapter 8 discusses on how unstructured data are used in big data processing. It includes Query processing performance evaluation in big data systems, the types od dirty data. Data cleansing is explained in detail with the steps in cleansing, exploratory data analysis, and data visualization. Hope this book on Managing Unstructured Data: NoSQL Database Essentials will provide a handy and useful reference book for teachers and students on Unstructured Database.

Modern Big Data Processing with Hadoop

Modern Big Data Processing with Hadoop
Author: V Naresh Kumar
Publisher: Packt Publishing Ltd
Total Pages: 390
Release: 2018-03-30
Genre: Computers
ISBN: 1787128814
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A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop Key Features -Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform -Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more -A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Book Description The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. What you will learn Build an efficient enterprise Big Data strategy centered around Apache Hadoop Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari Design effective streaming data pipelines and build your own enterprise search solutions Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset Plan, set up and administer your Hadoop cluster efficiently Who this book is for This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book.

Big Data Networked Storage Solution for Hadoop

Big Data Networked Storage Solution for Hadoop
Author: Prem Jain
Publisher: IBM Redbooks
Total Pages: 56
Release: 2013-07-12
Genre: Computers
ISBN: 0738451045
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This IBM® RedpaperTM provides a reference architecture, based on Apache Hadoop, to help businesses gain control over their data, meet tight service level agreements (SLAs) around their data applications, and turn data-driven insight into effective action. Big Data Networked Storage Solution for Hadoop delivers the capabilities for ingesting, storing, and managing large data sets with high reliability. IBM InfoSphere® Big InsightsTM provides an innovative analytics platform that processes and analyzes all types of data to turn large complex data into insight. IBM InfoSphere BigInsights brings the power of Hadoop to the enterprise. With built-in analytics, extensive integration capabilities, and the reliability, security and support that you require, IBM can help put your big data to work for you. This IBM Redpaper publication provides basic guidelines and best practices for how to size and configure Big Data Networked Storage Solution for Hadoop.