Overcome challenges in building transactional guarantees on rapidly changing data by using Apache Hudi. With this practical guide, data engineers, data architects, and software architects will discover how to seamlessly build an interoperable lakehouse from disparate data sources and deliver faster insights using your query engine of choice. Authors Shiyan Xu, Prashant Wason, Bhavani Sudha Saktheeswaran, and Rebecca Bilbro provide practical examples and insights to help you unlock the full potential of data lakehouses for different levels of analytics, from batch to interactive to streaming. You'll also learn how to evaluate storage choices and leverage built-in automated table optimizations to build, maintain, and operate production data applications. Understand the need for transactional data lakehouses and the challenges associated with building them Explore data ecosystem support provided by Apache Hudi for popular data sources and query engines Perform different write and read operations on Apache Hudi tables and effectively use them for various use cases, including batch and stream applications Apply different storage techniques and considerations such as indexing and clustering to maximize your lakehouse performance Build end-to-end incremental data pipelines using Apache Hudi for faster ingestion and fresher analytics

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services.

"Apache Hive Essentials" provides a focused guide to mastering the essential techniques of processing and analyzing big data with Apache Hive. What this Book will help me do Set up and configure a Hive environment for big data analysis. Compose effective queries using Hive's SQL-like language to extract insights. Optimize Hive performance to handle complex datasets efficiently. Implement data security and user-defined functions to extend capabilities. Integrate Hive with Hadoop tools for comprehensive data solutions. Author(s) Dayong Du, the author of "Apache Hive Essentials," has years of experience working with big data technologies and tools. With hands-on expertise in Hadoop and the entire ecosystem, he brings a practical and informed perspective to this complex field. His approach is to make these technologies accessible to developers and analysts of all levels. Who is it for? This book is perfect for data analysts, developers, or professionals familiar with SQL who are looking to start with Apache Hive for big data processing. It is suitable for those acquainted with Hadoop and its environment and want to expand their skills into efficient data querying and management. Readers should have an interest in how to leverage big data tools for real-world solutions.

Apache Hive Cookbook is a comprehensive resource for mastering Apache Hive, a tool that bridges the gap between SQL and Big Data processing. Through guided recipes, you'll acquire essential skills in Hive query development, optimization, and integration with modern big data frameworks. What this Book will help me do Design efficient Hive query structures for big data analytics. Optimize data storage and query execution using partitions and buckets. Integrate Hive seamlessly with frameworks like Spark and Hadoop. Understand and utilize the HiveQL syntax to perform advanced analytical processing. Implement practical solutions to secure, maintain, and scale Hive environments. Author(s) Hanish Bansal, Saurabh Chauhan, and Shrey Mehrotra bring their extensive expertise in big data technologies and Hive to this cookbook. With years of practical experience and deep technical knowledge, they offer a collection of solutions and best practices that reflect real-world use cases. Their commitment to clarity and depth makes this book an invaluable resource for exploring Hive to its fullest potential. Who is it for? This book is perfect for data professionals, engineers, and developers looking to enhance their capabilities in big data analytics using Hive. It caters to those with a foundational understanding of big data frameworks and some familiarity with SQL. Whether you're planning to optimize data handling or integrate Hive with other data tools, this guide helps you achieve your goals. Step into the world of efficient data analytics with Apache Hive through structured learning paths.

Apache Hive Essentials is the perfect guide for understanding and mastering Hive, the SQL-like big data query language built on top of Hadoop. With this book, you will gain the skills to effectively use Hive to analyze and manage large data sets. Whether you're a developer, data analyst, or just curious about big data, this hands-on guide will enhance your capabilities. What this Book will help me do Understand the core concepts of Hive and its relation to big data and Hadoop. Learn how to set up a Hive environment and integrate it with Hadoop. Master the SQL-like query functionalities of Hive to select, manipulate, and analyze data. Develop custom functions in Hive to extend its functionality for your own specific use cases. Discover best practices for optimizing Hive performance and ensuring data security. Author(s) Dayong Du is an expert in big data analytics with extensive experience in implementing and using tools like Hive in professional settings. Having worked on practical big data solutions, Dayong brings a wealth of knowledge and insights to his writing. His clear, approachable style makes complex topics accessible to readers. Who is it for? This book is ideal for developers, data analysts, and data engineers looking to leverage Hive for big data analysis. If you are familiar with SQL and Hadoop basics and aim to enhance your understanding of Hive, this book is for you. Beginners with some programming background eager to dive into big data technologies will also benefit. It's tailored for learners wanting actionable knowledge to advance their data processing skills.

Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect—HiveQL—to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem. This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use Hive to create, alter, and drop databases, tables, views, functions, and indexes Customize data formats and storage options, from files to external databases Load and extract data from tables—and use queries, grouping, filtering, joining, and other conventional query methods Gain best practices for creating user defined functions (UDFs) Learn Hive patterns you should use and anti-patterns you should avoid Integrate Hive with other data processing programs Use storage handlers for NoSQL databases and other datastores Learn the pros and cons of running Hive on Amazon’s Elastic MapReduce