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Title & Speakers	Event
Event O'Reilly Data Engineering Books 2019-01-31
Apache Spark Quick Start Guide 2019-01-31 Akash Grade – author , Shrey Mehrotra – author Dive into the world of scalable data processing with the "Apache Spark Quick Start Guide." This book offers a foundational introduction to Spark, empowering readers to harness its capabilities for big data processing. With clear explanations and hands-on examples, you'll learn to implement Spark applications that handle complex data tasks efficiently. What this Book will help me do Understand and implement Spark's RDDs and DataFrame APIs to process large datasets effectively. Set up a local development environment for Spark-based projects. Develop skills to debug and optimize slow-performing Spark applications. Harness built-in modules of Spark for SQL, streaming, and machine learning applications. Adopt best practices and optimization techniques for high-performance Spark applications. Author(s) Shrey Mehrotra is a seasoned software developer with expertise in big data technologies, particularly Apache Spark. With years of hands-on industry experience, Shrey focuses on making complex technical concepts accessible to all. Through his writing, he aims to share clear, practical guidance for developers of all levels. Who is it for? This guide is perfect for big data enthusiasts and professionals looking to learn Apache Spark's capabilities from scratch. It's aimed at data engineers interested in optimizing application performance and data scientists wanting to integrate machine learning with Spark. A basic familiarity with either Scala, Python, or Java is recommended. data data-engineering apache-spark AI/ML API Big Data Java Python Scala Spark SQL Data Streaming
Apache Hive Cookbook 2016-04-29 Saurabh Chauhan – author , Hanish Bansal – author , Shrey Mehrotra – author 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. data data-engineering Hadoop apache-hive Analytics Big Data Data Analytics Hive Spark SQL
Learning YARN 2015-08-28 Akhil Arora – author , Shrey Mehrotra – author "Learning YARN" is your comprehensive guide to master YARN, the resource management layer in the Hadoop ecosystem. Through the book, you'll leverage YARN's capabilities for big data processing, learning to deploy, manage, and scale Hadoop-YARN clusters. What this Book will help me do Understand the main features and benefits of the YARN framework. Gain experience managing Hadoop clusters of varying sizes. Learn to integrate YARN with domain-specific big data tools like Spark. Become skilled at administration and configuration of YARN. Develop and run your own YARN-based applications for distributed computing. Author(s) Akhil Arora and Shrey Mehrotra bring with them years of experience working in big data frameworks and technologies. With expertise in YARN specifically, they aim to bridge the gap for developers and administrators to learn and implement scalable big data solutions. Their extensive knowledge in cluster management and distributed data processing shines through in how this book is structured and detailed. Who is it for? This book is ideal for software developers, big data engineers, and system administrators interested in advancing their knowledge in resource management in Hadoop systems. If you have basic familiarity with Hadoop and need a deeper understanding or feature knowledge of YARN for professional growth, this book is tailored for you. It is also suitable for learners seeking to integrate big data platforms like Spark into YARN clusters. data data-engineering Hadoop yarn Big Data Spark

Apache Spark Quick Start Guide 2019-01-31

Akash Grade – author , Shrey Mehrotra – author

Dive into the world of scalable data processing with the "Apache Spark Quick Start Guide." This book offers a foundational introduction to Spark, empowering readers to harness its capabilities for big data processing. With clear explanations and hands-on examples, you'll learn to implement Spark applications that handle complex data tasks efficiently. What this Book will help me do Understand and implement Spark's RDDs and DataFrame APIs to process large datasets effectively. Set up a local development environment for Spark-based projects. Develop skills to debug and optimize slow-performing Spark applications. Harness built-in modules of Spark for SQL, streaming, and machine learning applications. Adopt best practices and optimization techniques for high-performance Spark applications. Author(s) Shrey Mehrotra is a seasoned software developer with expertise in big data technologies, particularly Apache Spark. With years of hands-on industry experience, Shrey focuses on making complex technical concepts accessible to all. Through his writing, he aims to share clear, practical guidance for developers of all levels. Who is it for? This guide is perfect for big data enthusiasts and professionals looking to learn Apache Spark's capabilities from scratch. It's aimed at data engineers interested in optimizing application performance and data scientists wanting to integrate machine learning with Spark. A basic familiarity with either Scala, Python, or Java is recommended.

data data-engineering apache-spark AI/ML API Big Data Java Python Scala Spark SQL Data Streaming

Apache Hive Cookbook 2016-04-29

Saurabh Chauhan – author , Hanish Bansal – author , Shrey Mehrotra – author

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.

data data-engineering Hadoop apache-hive Analytics Big Data Data Analytics Hive Spark SQL

Learning YARN 2015-08-28

Akhil Arora – author , Shrey Mehrotra – author

"Learning YARN" is your comprehensive guide to master YARN, the resource management layer in the Hadoop ecosystem. Through the book, you'll leverage YARN's capabilities for big data processing, learning to deploy, manage, and scale Hadoop-YARN clusters. What this Book will help me do Understand the main features and benefits of the YARN framework. Gain experience managing Hadoop clusters of varying sizes. Learn to integrate YARN with domain-specific big data tools like Spark. Become skilled at administration and configuration of YARN. Develop and run your own YARN-based applications for distributed computing. Author(s) Akhil Arora and Shrey Mehrotra bring with them years of experience working in big data frameworks and technologies. With expertise in YARN specifically, they aim to bridge the gap for developers and administrators to learn and implement scalable big data solutions. Their extensive knowledge in cluster management and distributed data processing shines through in how this book is structured and detailed. Who is it for? This book is ideal for software developers, big data engineers, and system administrators interested in advancing their knowledge in resource management in Hadoop systems. If you have basic familiarity with Hadoop and need a deeper understanding or feature knowledge of YARN for professional growth, this book is tailored for you. It is also suitable for learners seeking to integrate big data platforms like Spark into YARN clusters.

data data-engineering Hadoop yarn Big Data Spark

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