Data Engineering With Apache Hadoop And Hive
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Data Engineering with Apache Hadoop and Hive
Author: Matt Mueyon
language: en
Publisher: Independently Published
Release Date: 2024-04-09
Dive into the world of big data with "Data Engineering with Apache Hadoop and Hive," your comprehensive guide to mastering two of the most powerful technologies in the data engineering space. This book offers in-depth insights into the intricacies of Apache Hadoop and Hive, equipping you with the knowledge to store, manage, and analyze vast amounts of data efficiently. From setting up your first Hadoop cluster to executing advanced data analytics with HiveQL, each chapter builds upon the last, ensuring a solid understanding of the core concepts and advanced techniques. Learn how to leverage HDFS for scalable, reliable storage, exploit MapReduce for complex data processing, and unlock the full potential of data warehousing with Hive. For data engineers, analysts, and IT professionals aiming to enhance their skillset in big data technologies, this book is an essential resource. Through a blend of theoretical knowledge, practical insights, and real-world examples, you'll master data storage optimization, advanced Hive features, and best practices for secure and efficient data management. Prepare to tackle big data challenges with confidence and expertise with "Data Engineering with Apache Hadoop and Hive." Whether you're new to the field or looking to deepen your knowledge, this book will serve as your invaluable companion on your data engineering journey.
Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive
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.
Big Data Using Hadoop and Hive
Author: Nitin Kumar
language: en
Publisher: Mercury Learning and Information
Release Date: 2021-04
This book is the basic guide for developers, architects, engineers, and anyone who wants to start leveraging the open-source software Hadoop and Hive to build distributed, scalable concurrent big data applications. Hive will be used for reading, writing, and managing the large, data set files. The book is a concise guide on getting started with an overall understanding on Apache Hadoop and Hive and how they work together to speed up development with minimal effort. It will refer to simple concepts and examples, as they are likely to be the best teaching aids. It will explain the logic, code, and configurations needed to build a successful, distributed, concurrent application, as well as the reason behind those decisions. FEATURES: Shows how to leverage the open-source software Hadoop and Hive to build distributed, scalable, concurrent big data applications Includes material on Hive architecture with various storage types and the Hive query language Features a chapter on big data and how Hadoop can be used to solve the changes around it Explains the basic Hadoop setup, configuration, and optimization