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Author: Dayong Du
Publisher: Packt Publishing Ltd
ISBN: 1782175059
Category : Computers
Languages : en
Pages : 208
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Book Description
If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.
Author: Dayong Du
Publisher: Packt Publishing Ltd
ISBN: 1782175059
Category : Computers
Languages : en
Pages : 208
Get Book Here
Book Description
If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.
Author: Douglas Eadline
Publisher: Addison-Wesley Professional
ISBN: 0134049993
Category : Computers
Languages : en
Pages : 767
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Book Description
Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters Exploring the Hadoop Distributed File System (HDFS) Understanding the essentials of MapReduce and YARN application programming Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase Observing application progress, controlling jobs, and managing workflows Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark
Author: Hrishikesh Vijay Karambelkar
Publisher: Packt Publishing Ltd
ISBN: 1788994345
Category : Computers
Languages : en
Pages : 214
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Book Description
A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem Key FeaturesSet up, configure and get started with Hadoop to get useful insights from large data setsWork with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the new features introduced in Hadoop 3Book Description Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. What you will learnStore and analyze data at scale using HDFS, MapReduce and YARNInstall and configure Hadoop 3 in different modesUse Yarn effectively to run different applications on Hadoop based platformUnderstand and monitor how Hadoop cluster is managedConsume streaming data using Storm, and then analyze it using SparkExplore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and KafkaWho this book is for Aspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.
Author: Darren Lee
Publisher: Packt Publishing
ISBN: 9781782169475
Category : Computers
Languages : en
Pages : 76
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Book Description
Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks.This book provides quick recipes for using Hive to read data in various formats, efficiently querying this data, and extending Hive with any custom functions you may need to insert your own logic into the data pipeline.This book is written for data analysts and developers who want to use their current knowledge of SQL to be more productive with Hadoop. It assumes that readers are comfortable writing SQL queries and are familiar with Hadoop at the level of the classic WordCount example.
Author: Frank J. Ohlhorst
Publisher: John Wiley & Sons
ISBN: 1118147596
Category : Business & Economics
Languages : en
Pages : 176
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Book Description
Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.
Author: Simhadri Govindappa
Publisher: Orange Education Pvt Ltd
ISBN: 8197396574
Category : Computers
Languages : en
Pages : 367
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Book Description
TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index
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: Ofer Mendelevitch
Publisher: Addison-Wesley Professional
ISBN: 0134029720
Category : Computers
Languages : en
Pages : 463
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Book Description
The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language
Author: Jagat Jasjit Singh
Publisher: Packt Publishing Ltd
ISBN: 1785888463
Category : Computers
Languages : en
Pages : 165
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Book Description
Unleash the power of Apache Oozie to create and manage your big data and machine learning pipelines in one go About This Book Teaches you everything you need to know to get started with Apache Oozie from scratch and manage your data pipelines effortlessly Learn to write data ingestion workflows with the help of real-life examples from the author's own personal experience Embed Spark jobs to run your machine learning models on top of Hadoop Who This Book Is For If you are an expert Hadoop user who wants to use Apache Oozie to handle workflows efficiently, this book is for you. This book will be handy to anyone who is familiar with the basics of Hadoop and wants to automate data and machine learning pipelines. What You Will Learn Install and configure Oozie from source code on your Hadoop cluster Dive into the world of Oozie with Java MapReduce jobs Schedule Hive ETL and data ingestion jobs Import data from a database through Sqoop jobs in HDFS Create and process data pipelines with Pig, hive scripts as per business requirements. Run machine learning Spark jobs on Hadoop Create quick Oozie jobs using Hue Make the most of Oozie's security capabilities by configuring Oozie's security In Detail As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities is booming exponentially. This calls for data management. Hadoop caters to this need. Oozie fulfils this necessity for a scheduler for a Hadoop job by acting as a cron to better analyze data. Apache Oozie Essentials starts off with the basics right from installing and configuring Oozie from source code on your Hadoop cluster to managing your complex clusters. You will learn how to create data ingestion and machine learning workflows. This book is sprinkled with the examples and exercises to help you take your big data learning to the next level. You will discover how to write workflows to run your MapReduce, Pig ,Hive, and Sqoop scripts and schedule them to run at a specific time or for a specific business requirement using a coordinator. This book has engaging real-life exercises and examples to get you in the thick of things. Lastly, you'll get a grip of how to embed Spark jobs, which can be used to run your machine learning models on Hadoop. By the end of the book, you will have a good knowledge of Apache Oozie. You will be capable of using Oozie to handle large Hadoop workflows and even improve the availability of your Hadoop environment. Style and approach This book is a hands-on guide that explains Oozie using real-world examples. Each chapter is blended beautifully with fundamental concepts sprinkled in-between case study solution algorithms and topped off with self-learning exercises.
Author: Ian Foster
Publisher: MIT Press
ISBN: 0262037246
Category : Computers
Languages : en
Pages : 391
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Book Description
A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.
