Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 202
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Book Description
Unleash the Power of Big Data Processing In the realm of big data, the MapReduce framework stands as a cornerstone, enabling the processing of massive datasets with unparalleled efficiency. "Mastering the MapReduce Framework" is your comprehensive guide to understanding and harnessing the capabilities of this transformative technology, equipping you with the skills needed to navigate the landscape of large-scale data processing. About the Book: As the volume of data continues to grow exponentially, traditional data processing methods fall short. The MapReduce framework emerges as a powerful solution, allowing organizations to process and analyze vast datasets in parallel, thereby unlocking insights and accelerating decision-making. "Mastering the MapReduce Framework" provides a deep dive into this technology, catering to both beginners and experienced professionals seeking to maximize their proficiency in big data processing. Key Features: Foundation Building: Begin by comprehending the fundamental concepts underlying MapReduce. Understand how the framework breaks down complex tasks into smaller, manageable components that can be processed concurrently. Parallel Processing: Dive into the intricacies of parallel processing, a cornerstone of MapReduce. Learn how data is partitioned and distributed across a cluster of machines, enabling lightning-fast computation. Map and Reduce Functions: Grasp the significance of map and reduce functions in the MapReduce paradigm. Learn how to structure these functions to transform and aggregate data efficiently. Hadoop Ecosystem: Explore the Hadoop ecosystem, which houses the MapReduce framework. Understand how Hadoop integrates with other tools to create a comprehensive big data processing environment. Optimizing Performance: Discover techniques for optimizing MapReduce performance. Learn about data locality, combiners, and partitioners that enhance efficiency and reduce resource consumption. Real-World Use Cases: Gain insights into real-world applications of MapReduce across industries. From web log analysis to recommendation systems, explore how the framework powers data-driven solutions. Challenges and Solutions: Explore the challenges of working with MapReduce, such as debugging and handling skewed data. Master strategies to address these challenges and ensure smooth execution. Why This Book Matters: In a data-driven world, the ability to process and extract insights from massive datasets is a competitive advantage. "Mastering the MapReduce Framework" empowers data engineers, analysts, and technology enthusiasts to tap into the potential of big data processing, enabling them to drive innovation and make data-driven decisions with confidence. Who Should Read This Book: Data Engineers: Enhance your big data processing skills with a deep understanding of MapReduce. Data Analysts: Grasp the principles that power large-scale data analysis and gain insights from big data. Technology Enthusiasts: Dive into the world of big data processing and stay ahead of emerging trends. Harness the Power of Big Data Processing: The era of big data requires sophisticated processing tools, and the MapReduce framework stands as a pioneer in this realm. "Mastering the MapReduce Framework" equips you with the knowledge needed to harness the power of MapReduce, unleashing the potential of big data processing and enabling you to navigate the complexities of large-scale data analysis with ease. Your journey to mastering the art of big data processing begins here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Chanchal Singh
Publisher: Packt Publishing Ltd
ISBN: 1788628322
Category : Computers
Languages : en
Pages : 531
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Book Description
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.
Author: Jimmy Lin
Publisher: Springer Nature
ISBN: 3031021363
Category : Computers
Languages : en
Pages : 171
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Book Description
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 194
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Book Description
Unleash the Power of Big Data Processing with Apache Hadoop Ecosystem Are you ready to embark on a journey into the world of big data processing and analysis using Apache Hadoop? "Mastering Apache Hadoop" is your comprehensive guide to understanding and harnessing the capabilities of Hadoop for processing and managing massive datasets. Whether you're a data engineer seeking to optimize processing pipelines or a business analyst aiming to extract insights from large data, this book equips you with the knowledge and tools to master the art of Hadoop-based data processing. Key Features: 1. Deep Dive into Hadoop Ecosystem: Immerse yourself in the core components and concepts of the Apache Hadoop ecosystem. Understand the architecture, components, and functionalities that make Hadoop a powerful platform for big data. 2. Installation and Configuration: Master the art of installing and configuring Hadoop on various platforms. Learn about cluster setup, resource management, and configuration settings for optimal performance. 3. Hadoop Distributed File System (HDFS): Uncover the power of HDFS for distributed storage and data management. Explore concepts like replication, fault tolerance, and data placement to ensure data durability. 4. MapReduce and Data Processing: Delve into MapReduce, the core data processing paradigm in Hadoop. Learn how to write MapReduce jobs, optimize performance, and leverage parallel processing for efficient data analysis. 5. Data Ingestion and ETL: Discover techniques for ingesting and transforming data in Hadoop. Explore tools like Apache Sqoop and Apache Flume for extracting data from various sources and loading it into Hadoop. 6. Data Querying and Analysis: Master querying and analyzing data using Hadoop. Learn about Hive, Pig, and Spark SQL for querying structured and semi-structured data, and uncover insights that drive informed decisions. 7. Data Storage Formats: Explore data storage formats optimized for Hadoop. Learn about Avro, Parquet, and ORC, and understand how to choose the right format for efficient storage and retrieval. 8. Batch and Stream Processing: Uncover strategies for batch and real-time data processing in Hadoop. Learn how to use Apache Spark and Apache Flink to process data in both batch and streaming modes. 9. Data Visualization and Reporting: Discover techniques for visualizing and reporting on Hadoop data. Explore integration with tools like Apache Zeppelin and Tableau to create compelling visualizations. 10. Real-World Applications: Gain insights into real-world use cases of Apache Hadoop across industries. From financial analysis to social media sentiment analysis, explore how organizations are leveraging Hadoop's capabilities for data-driven innovation. Who This Book Is For: "Mastering Apache Hadoop" is an essential resource for data engineers, analysts, and IT professionals who want to excel in big data processing using Hadoop. Whether you're new to Hadoop or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of big data technology.
Author: Peter Jones
Publisher: Walzone Press
ISBN:
Category : Computers
Languages : en
Pages : 195
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Book Description
Immerse yourself in the realm of big data with "Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive," your definitive guide to mastering two of the most potent technologies in the data engineering landscape. This book provides comprehensive insights into the complexities of Apache Hadoop and Hive, equipping you with the expertise to store, manage, and analyze vast amounts of data with precision. From setting up your initial Hadoop cluster to performing sophisticated data analytics with HiveQL, each chapter methodically builds on the previous one, ensuring a robust understanding of both fundamental concepts and advanced methodologies. Discover how to harness HDFS for scalable and reliable storage, utilize MapReduce for intricate data processing, and fully exploit data warehousing capabilities with Hive. Targeted at data engineers, analysts, and IT professionals striving to advance their proficiency in big data technologies, this book is an indispensable resource. Through a blend of theoretical insights, practical knowledge, and real-world examples, you will master data storage optimization, advanced Hive functionalities, and best practices for secure and efficient data management. Equip yourself to confront big data challenges with confidence and skill with "Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive." Whether you're a novice in the field or seeking to expand your expertise, this book will be your invaluable guide on your data engineering journey.
Author: J. T. Wolohan
Publisher: Manning Publications
ISBN: 9781617296239
Category :
Languages : en
Pages : 350
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Book Description
With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author: Mahmoud Parsian
Publisher: "O'Reilly Media, Inc."
ISBN: 1491906154
Category : Computers
Languages : en
Pages : 778
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Book Description
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)
Author: Tomcy John
Publisher: Packt Publishing Ltd
ISBN: 1787282651
Category : Computers
Languages : en
Pages : 585
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Book Description
A practical guide to implementing your enterprise data lake using Lambda Architecture as the base About This Book Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base Delve into the big data technologies required to meet modern day business strategies A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases Who This Book Is For Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you. What You Will Learn Build an enterprise-level data lake using the relevant big data technologies Understand the core of the Lambda architecture and how to apply it in an enterprise Learn the technical details around Sqoop and its functionalities Integrate Kafka with Hadoop components to acquire enterprise data Use flume with streaming technologies for stream-based processing Understand stream- based processing with reference to Apache Spark Streaming Incorporate Hadoop components and know the advantages they provide for enterprise data lakes Build fast, streaming, and high-performance applications using ElasticSearch Make your data ingestion process consistent across various data formats with configurability Process your data to derive intelligence using machine learning algorithms In Detail The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake. Style and approach The book takes a pragmatic approach, showing ways to leverage big data technologies and lambda architecture to build an enterprise-level data lake.
Author: Tom White
Publisher: "O'Reilly Media, Inc."
ISBN: 1449338771
Category : Computers
Languages : en
Pages : 687
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Book Description
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
Author: Rajkumar Buyya
Publisher: Newnes
ISBN: 0124095399
Category : Computers
Languages : en
Pages : 469
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Book Description
Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won't live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout. - Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment - Real-world case studies include scientific, business, and energy-efficiency considerations
