Get a solid grounding in Apache Oozie, the workflow scheduler system for managing Hadoop jobs. With this hands-on guide, two experienced Hadoop practitioners walk you through the intricacies of this powerful and flexible platform, with numerous examples and real-world use cases. Once you set up your Oozie server, you’ll dive into techniques for writing and coordinating workflows, and learn how to write complex data pipelines. Advanced topics show you how to handle shared libraries in Oozie, as well as how to implement and manage Oozie’s security capabilities. Install and configure an Oozie server, and get an overview of basic concepts Journey through the world of writing and configuring workflows Learn how the Oozie coordinator schedules and executes workflows based on triggers Understand how Oozie manages data dependencies Use Oozie bundles to package several coordinator apps into a data pipeline Learn about security features and shared library management Implement custom extensions and write your own EL functions and actions Debug workflows and manage Oozie’s operational details
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.
Bigdata is one of the most demanding markets in the IT sector. If you are an administrator or a have a passion for knowing the internal configurations of Hadoop, then this book is for you. This book enables a professional to learn about Hadoop in terms of installation, configuration, and management. This book will help the reader to jumpstart with Hadoop frameworks, its eco-system components and slowly progress towards learning the administration part of Hadoop. The level of this book goes from beginner to intermediate with 70% hands-on exercises. Some of the techniques that you will learn include, • Installation and configuration of Hadoop cluster • Performing Hadoop Cluster Upgrade • Understanding and implementing HDFS Federation • Understanding and Implementing High Availability • Implementing HA on a Federated Cluster • Zookeeper CLI • Apache Hive Installation and Security • HBase Multi-master setup • Oozie installation, configuration and job submission • Setting up HDFS Quotas • Setting up HDFS NFS gateway • Understanding and implementing rolling upgrade and much more.
The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. The Comprehensive, Up-to-Date Apache Hadoop Administration Handbook and Reference “Sam Alapati has worked with production Hadoop clusters for six years. His unique depth of experience has enabled him to write the go-to resource for all administrators looking to spec, size, expand, and secure production Hadoop clusters of any size.” —Paul Dix, Series Editor In Expert Hadoop® Administration, leading Hadoop administrator Sam R. Alapati brings together authoritative knowledge for creating, configuring, securing, managing, and optimizing production Hadoop clusters in any environment. Drawing on his experience with large-scale Hadoop administration, Alapati integrates action-oriented advice with carefully researched explanations of both problems and solutions. He covers an unmatched range of topics and offers an unparalleled collection of realistic examples. Alapati demystifies complex Hadoop environments, helping you understand exactly what happens behind the scenes when you administer your cluster. You’ll gain unprecedented insight as you walk through building clusters from scratch and configuring high availability, performance, security, encryption, and other key attributes. The high-value administration skills you learn here will be indispensable no matter what Hadoop distribution you use or what Hadoop applications you run. Understand Hadoop’s architecture from an administrator’s standpoint Create simple and fully distributed clusters Run MapReduce and Spark applications in a Hadoop cluster Manage and protect Hadoop data and high availability Work with HDFS commands, file permissions, and storage management Move data, and use YARN to allocate resources and schedule jobs Manage job workflows with Oozie and Hue Secure, monitor, log, and optimize Hadoop Benchmark and troubleshoot Hadoop
Internet of Things (IoT) refers to physical and virtual objects that have unique identities and are connected to the internet to facilitate intelligent applications that make energy, logistics, industrial control, retail, agriculture and many other domains "smarter". Internet of Things is a new revolution of the Internet that is rapidly gathering momentum driven by the advancements in sensor networks, mobile devices, wireless communications, networking and cloud technologies. Experts forecast that by the year 2020 there will be a total of 50 billion devices/things connected to the internet. This book is written as a textbook on Internet of Things for educational programs at colleges and universities, and also for IoT vendors and service providers who may be interested in offering a broader perspective of Internet of Things to accompany their own customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. Like our companion book on Cloud Computing, we have tried to write a comprehensive book that transfers knowledge through an immersive "hands on" approach, where the reader is provided the necessary guidance and knowledge to develop working code for real-world IoT applications. Additional support is available at the book's website: www.internet-of-things-book.com Organization The book is organized into 3 main parts, comprising of a total of 11 chapters. Part I covers the building blocks of Internet of Things (IoTs) and their characteristics. A taxonomy of IoT systems is proposed comprising of various IoT levels with increasing levels of complexity. Domain specific Internet of Things and their real-world applications are described. A generic design methodology for IoT is proposed. An IoT system management approach using NETCONF-YANG is described. Part II introduces the reader to the programming aspects of Internet of Things with a view towards rapid prototyping of complex IoT applications. We chose Python as the primary programming language for this book, and an introduction to Python is also included within the text to bring readers to a common level of expertise. We describe packages, frameworks and cloud services including the WAMP-AutoBahn, Xively cloud and Amazon Web Services which can be used for developing IoT systems. We chose the Raspberry Pi device for the examples in this book. Reference architectures for different levels of IoT applications are examined in detail. Case studies with complete source code for various IoT domains including home automation, smart environment, smart cities, logistics, retail, smart energy, smart agriculture, industrial control and smart health, are described. Part III introduces the reader to advanced topics on IoT including IoT data analytics and Tools for IoT. Case studies on collecting and analyzing data generated by Internet of Things in the cloud are described.
Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop. Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems. Transfer data from a single database table into your Hadoop ecosystem Keep table data and Hadoop in sync by importing data incrementally Import data from more than one database table Customize transferred data by calling various database functions Export generated, processed, or backed-up data from Hadoop to your database Run Sqoop within Oozie, Hadoop’s specialized workflow scheduler Load data into Hadoop’s data warehouse (Hive) or database (HBase) Handle installation, connection, and syntax issues common to specific database vendors
Learn to use Apache Pig to develop lightweight big data applications easily and quickly. This book shows you many optimization techniques and covers every context where Pig is used in big data analytics. Beginning Apache Pig shows you how Pig is easy to learn and requires relatively little time to develop big data applications.The book is divided into four parts: the complete features of Apache Pig; integration with other tools; how to solve complex business problems; and optimization of tools.You'll discover topics such as MapReduce and why it cannot meet every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be created; submitting Pig jobs using Hue; and working with Oozie. You'll also see how to extend the framework by writing UDFs and custom load, store, and filter functions. Finally you'll cover different optimization techniques such as gathering statistics about a Pig script, joining strategies, parallelism, and the role of data formats in good performance. What You Will Learn• Use all the features of Apache Pig• Integrate Apache Pig with other tools• Extend Apache Pig• Optimize Pig Latin code• Solve different use cases for Pig LatinWho This Book Is ForAll levels of IT professionals: architects, big data enthusiasts, engineers, developers, and big data administrators
Master AWS data engineering services and techniques for orchestrating pipelines, building layers, and managing migrations Key Features Get up to speed with the different AWS technologies for data engineering Learn the different aspects and considerations of building data lakes, such as security, storage, and operations Get hands on with key AWS services such as Glue, EMR, Redshift, QuickSight, and Athena for practical learning Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPerforming data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction. Through clear explanations and hands-on exercises, you’ll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you’ll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges. Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.What you will learn Define your centralized data lake solution, and secure and operate it at scale Identify the most suitable AWS solution for your specific needs Build data pipelines using multiple ETL technologies Discover how to handle data orchestration and governance Explore how to build a high-performing data serving layer Delve into DevOps and data quality best practices Migrate your data from on-premises to AWS Who this book is for If you're involved in designing, building, or overseeing data solutions on AWS, this book provides proven strategies for addressing challenges in large-scale data environments. Data engineers as well as big data professionals looking to enhance their understanding of AWS features for optimizing their workflow, even if they're new to the platform, will find value. Basic familiarity with AWS security (users and roles) and command shell is recommended.
"Apache Hadoop is helping drive the Big Data revolution. Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data. And now in Apache HadoopTM YARN, two Hadoop technical leaders show you how to develop new applications and adapt existing code to fully leverage these revolutionary advances." -- From the Amazon
