Mastering Apache Storm is your step-by-step guide to mastering real-time data streaming with this robust framework. You'll learn how to process big data efficiently and integrate Apache Storm with popular technologies like Kafka, HBase, and Redis to maximize its potential. This book walks you through from basic concepts to advanced implementations of Apache Storm in real-world scenarios. What this Book will help me do Understand the core features and operation of Apache Storm for real-time data streaming. Integrate Apache Storm with other Big Data frameworks like Kafka, HBase, Redis, and Hadoop. Effectively deploy and manage multi-node Apache Storm clusters in real-world environments. Monitor and analyze your data streams and system health effectively using built-in and external tools. Learn to implement fault-tolerant, scalable, and distributed stream processing applications in Apache Storm. Author(s) None Jain is an experienced software developer and technical instructor specializing in distributed systems and real-time data processing. With years of experience working with Apache Storm and related technologies, their teachings focus on practical, hands-on learning to equip readers with actionable skills. Who is it for? This book is ideal for Java developers aspiring to build expertise in real-time data streaming and distributed processing applications using Apache Storm. Beginners can start with the fundamentals provided, while those with prior knowledge can delve into intermediate and advanced implementations.

Delve into mastering big data processing with 'Apache Spark 2.x for Java Developers.' This book provides a practical guide to implementing Apache Spark using the Java APIs, offering a unique opportunity for Java developers to leverage Spark's powerful framework without transitioning to Scala. What this Book will help me do Learn how to process data from formats like XML, JSON, CSV using Spark Core. Implement real-time analytics using Spark Streaming and third-party tools like Kafka. Understand data querying with Spark SQL and master SQL schema processing. Apply machine learning techniques with Spark MLlib to real-world scenarios. Explore graph processing and analytics using Spark GraphX. Author(s) None Kumar and None Gulati, experienced professionals in Java development and big data, bring their wealth of practical experience and passion for teaching to this book. With a clear and concise writing style, they aim to simplify Spark for Java developers, making big data approachable. Who is it for? This book is perfect for Java developers who are eager to expand their skillset into big data processing with Apache Spark. Whether you are a seasoned Spark user or first diving into big data concepts, this book meets you at your level. With practical examples and straightforward explanations, you can unlock the potential of Spark in real-world scenarios.

Streaming Data introduces the concepts and requirements of streaming and real-time data systems. The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data. About the Technology As humans, we're constantly filtering and deciphering the information streaming toward us. In the same way, streaming data applications can accomplish amazing tasks like reading live location data to recommend nearby services, tracking faults with machinery in real time, and sending digital receipts before your customers leave the shop. Recent advances in streaming data technology and techniques make it possible for any developer to build these applications if they have the right mindset. This book will let you join them. About the Book Streaming Data is an idea-rich tutorial that teaches you to think about efficiently interacting with fast-flowing data. Through relevant examples and illustrated use cases, you'll explore designs for applications that read, analyze, share, and store streaming data. Along the way, you'll discover the roles of key technologies like Spark, Storm, Kafka, Flink, RabbitMQ, and more. This book offers the perfect balance between big-picture thinking and implementation details. What's Inside The right way to collect real-time data Architecting a streaming pipeline Analyzing the data Which technologies to use and when About the Reader Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required. About the Author Andrew Psaltis is a software engineer focused on massively scalable real-time analytics. Quotes The definitive book if you want to master the architecture of an enterprise-grade streaming application. - Sergio Fernandez Gonzalez, Accenture A thorough explanation and examination of the different systems, strategies, and tools for streaming data implementations. - Kosmas Chatzimichalis, Mach 7x A well-structured way to learn about streaming data and how to put it into practice in modern real-time systems. - Giuliano Araujo Bertoti, FATEC This book is all you need to understand what streaming is all about! - Carlos Curotto, Globant

JSON is becoming the backbone for meaningful data interchange over the internet. This format is now supported by an entire ecosystem of standards, tools, and technologies for building truly elegant, useful, and efficient applications. With this hands-on guide, author and architect Tom Marrs shows you how to build enterprise-class applications and services by leveraging JSON tooling and message/document design. JSON at Work provides application architects and developers with guidelines, best practices, and use cases, along with lots of real-world examples and code samples. You’ll start with a comprehensive JSON overview, explore the JSON ecosystem, and then dive into JSON’s use in the enterprise. Get acquainted with JSON basics and learn how to model JSON data Learn how to use JSON with Node.js, Ruby on Rails, and Java Structure JSON documents with JSON Schema to design and test APIs Search the contents of JSON documents with JSON Search tools Convert JSON documents to other data formats with JSON Transform tools Compare JSON-based hypermedia formats, including HAL and jsonapi Leverage MongoDB to store and access JSON documents Use Apache Kafka to exchange JSON-based messages between services

Messaging is one of the more poorly understood areas of IT; most developers and architects have only a passing familiarity with how broker-based messaging technologies work. This practical report not only helps you get up to speed on the essentials of messaging, but also compares two of today’s most popular messaging technologies—Apache ActiveMQ and Apache Kafka. Author and consultant Jakub Korab describes use cases and design choices that lead developers to very different approaches for developing message-based systems. You’ll come away with a high-level understanding of both ActiveMQ and Kafka, including how they should and should not be used, how they handle concerns such as throughput and high-availability, and what to look out for when considering other messaging technologies in future. Understand the types of problems that messaging systems address Explore three primary messaging patterns: point-to-point, publish-subscribe, and a hybrid of both Dive into ActiveMQ, a classic broker-centric design implemented through Java libraries that works for a broad range of messaging use cases Examine Kafka, a distributed system that can be scaled to provide massive performance and fault tolerance through replication Learn the mechanical complexities that message-based systems need to address, and some patterns you can apply to deal with those complexities

Discover how to harness the power of Apache Spark 2.x for your Big Data processing projects. In this book, you will explore over 70 cloud-ready recipes that will guide you to perform distributed data analytics, structured streaming, machine learning, and much more. What this Book will help me do Effectively install and configure Apache Spark with various cluster managers and platforms. Set up and utilize development environments tailored for Spark applications. Operate on schema-aware data using RDDs, DataFrames, and Datasets. Perform real-time streaming analytics with sources such as Apache Kafka. Leverage MLlib for supervised learning, unsupervised learning, and recommendation systems. Author(s) None Yadav is a seasoned data engineer with a deep understanding of Big Data tools and technologies, particularly Apache Spark. With years of experience in the field of distributed computing and data analysis, Yadav brings practical insights and techniques to enrich the learning experience of readers. Who is it for? This book is ideal for data engineers, data scientists, and Big Data professionals who are keen to enhance their Apache Spark 2.x skills. If you're working with distributed processing and want to solve complex data challenges, this book addresses practical problems. Note that a basic understanding of Scala is recommended to get the most out of this resource.

"Data Lake for Enterprises" is a comprehensive guide to building data lakes using the Lambda Architecture. It introduces big data technologies like Hadoop, Spark, and Flume, showing how to use them effectively to manage and leverage enterprise-scale data. You'll gain the skills to design and implement data systems that handle complex data challenges. What this Book will help me do Master the use of Lambda Architecture to create scalable and effective data management systems. Understand and implement technologies like Hadoop, Spark, Kafka, and Flume in an enterprise data lake. Integrate batch and stream processing techniques using big data tools for comprehensive data analysis. Optimize data lakes for performance and reliability with practical insights and techniques. Implement real-world use cases of data lakes and machine learning for predictive data insights. Author(s) None Mishra, None John, and Pankaj Misra are recognized experts in big data systems with a strong background in designing and deploying data solutions. With a clear and methodical teaching style, they bring years of experience to this book, providing readers with the tools and knowledge required to excel in enterprise big data initiatives. Who is it for? This book is ideal for software developers, data architects, and IT professionals looking to integrate a data lake strategy into their enterprises. It caters to readers with a foundational understanding of Java and big data concepts, aiming to advance their practical knowledge of building scalable data systems. If you're eager to delve into cutting-edge technologies and transform enterprise data management, this book is for you.

Fast Data Processing Systems with SMACK Stack introduces you to the SMACK stack-a combination of Spark, Mesos, Akka, Cassandra, and Kafka. You will learn to integrate these technologies to build scalable, efficient, and real-time data processing platforms tailored for solving critical business challenges. What this Book will help me do Understand the concepts of fast data pipelines and design scalable architectures using the SMACK stack Gain expertise in functional programming with Scala and leverage its power in data processing tasks Build and optimize distributed databases using Apache Cassandra for scaling extensively Deploy and manage real-time data streams using Apache Kafka to handle massive messaging workloads Implement cost-effective cluster infrastructures with Apache Mesos for efficient resource utilization Author(s) None Estrada is an expert in distributed systems and big data technologies. With years of experience implementing SMACK-based solutions across industries, Estrada offers a practical viewpoint to designing scalable systems. Their blend of theoretical knowledge and applied practices ensures readers receive actionable guidance. Who is it for? This book is perfect for software developers, data engineers, or data scientists looking to deepen their understanding of real-time data processing systems. If you have a foundational knowledge of the technologies in the SMACK stack or wish to learn how to combine these cutting-edge tools to solve complex problems, this is for you. Readers with an interest in building efficient big data solutions will find tremendous value here.

Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout, and Apache Solr. From setting up the environment to running sample applications each chapter in this book is a practical tutorial on using an Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects, MapReduce and HDFS, and none discusses the other Apache Hadoop ecosystem projects and how they all work together as a cohesive big data development platform. What You Will Learn: Set up the environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5 Run a MapReduce job Store data with Apache Hive, and Apache HBase Index data in HDFS with Apache Solr Develop a Kafka messaging system Stream Logs to HDFS with Apache Flume Transfer data from MySQL database to Hive, HDFS, and HBase with Sqoop Create a Hive table over Apache Solr Develop a Mahout User Recommender System Who This Book Is For: Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.

Learn how to integrate full-stack open source big data architecture and to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer. Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses. Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer: What You'll Learn The language: Scala The engine: Spark (SQL, MLib, Streaming, GraphX) The container: Mesos, Docker The view: Akka The storage: Cassandra The message broker: Kafka What You Will Learn: Make big data architecture without using complex Greek letter architectures Build a cheap but effective cluster infrastructure Make queries, reports, and graphs that business demands Manage and exploit unstructured and No-SQL data sources Use tools to monitor the performance of your architecture Integrate all technologies and decide which ones replace and which ones reinforce Who This Book Is For Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer

Dive into the world of big data with "Big Data Analytics: Real Time Analytics Using Apache Spark and Hadoop." This comprehensive guide introduces readers to the fundamentals and practical applications of Apache Spark and Hadoop, covering essential topics like Spark SQL, DataFrames, structured streaming, and more. Learn how to harness the power of real-time analytics and big data tools effectively. What this Book will help me do Master the key components of Apache Spark and Hadoop ecosystems, including Spark SQL and MapReduce. Gain an understanding of DataFrames, DataSets, and structured streaming for seamless data handling. Develop skills in real-time analytics using Spark Streaming and technologies like Kafka and HBase. Learn to implement machine learning models using Spark's MLlib and ML Pipelines. Explore graph analytics with GraphX and leverage data visualization tools like Jupyter and Zeppelin. Author(s) Venkat Ankam, an expert in big data technologies, has years of experience working with Apache Hadoop and Spark. As an educator and technical consultant, Venkat has enabled numerous professionals to gain critical insights into big data ecosystems. With a pragmatic approach, his writings aim to guide readers through complex systems in a structured and easy-to-follow manner. Who is it for? This book is perfect for data analysts, data scientists, software architects, and programmers aiming to expand their knowledge of big data analytics. Readers should ideally have a basic programming background in languages like Python, Scala, R, or SQL. Prior hands-on experience with big data environments is not necessary but is an added advantage. This guide is created to cater to a range of skill levels, from beginners to intermediate learners.

Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility. This book’s straightforward, step-by-step approach shows you how to deploy, program, optimize, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data. Learn how to • Discover what Apache Spark does and how it fits into the Big Data landscape • Deploy and run Spark locally or in the cloud • Interact with Spark from the shell • Make the most of the Spark Cluster Architecture • Develop Spark applications with Scala and functional Python • Program with the Spark API, including transformations and actions • Apply practical data engineering/analysis approaches designed for Spark • Use Resilient Distributed Datasets (RDDs) for caching, persistence, and output • Optimize Spark solution performance • Use Spark with SQL (via Spark SQL) and with NoSQL (via Cassandra) • Leverage cutting-edge functional programming techniques • Extend Spark with streaming, R, and Sparkling Water • Start building Spark-based machine learning and graph-processing applications • Explore advanced messaging technologies, including Kafka • Preview and prepare for Spark’s next generation of innovations Instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you're finished, you'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.

HBase is a remarkable tool for indexing mass volumes of data, but getting started with this distributed database and its ecosystem can be daunting. With this hands-on guide, you’ll learn how to architect, design, and deploy your own HBase applications by examining real-world solutions. Along with HBase principles and cluster deployment guidelines, this book includes in-depth case studies that demonstrate how large companies solved specific use cases with HBase. Authors Jean-Marc Spaggiari and Kevin O’Dell also provide draft solutions and code examples to help you implement your own versions of those use cases, from master data management (MDM) and document storage to near real-time event processing. You’ll also learn troubleshooting techniques to help you avoid common deployment mistakes. Learn exactly what HBase does, what its ecosystem includes, and how to set up your environment Explore how real-world HBase instances were deployed and put into production Examine documented use cases for tracking healthcare claims, digital advertising, data management, and product quality Understand how HBase works with tools and techniques such as Spark, Kafka, MapReduce, and the Java API Learn how to identify the causes and understand the consequences of the most common HBase issues

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you’ll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm. Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases. Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layer New messaging technologies, including Apache Kafka and MapR Streams, with links to sample code Technology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache Apex How stream-based architectures are helpful to support microservices Specific use cases such as fraud detection and geo-distributed data streams Ted Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning. Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman.

The professional's one-stop guide to this open-source, Java-based big data framework Professional Hadoop is the complete reference and resource for experienced developers looking to employ Apache Hadoop in real-world settings. Written by an expert team of certified Hadoop developers, committers, and Summit speakers, this book details every key aspect of Hadoop technology to enable optimal processing of large data sets. Designed expressly for the professional developer, this book skips over the basics of database development to get you acquainted with the framework's processes and capabilities right away. The discussion covers each key Hadoop component individually, culminating in a sample application that brings all of the pieces together to illustrate the cooperation and interplay that make Hadoop a major big data solution. Coverage includes everything from storage and security to computing and user experience, with expert guidance on integrating other software and more. Hadoop is quickly reaching significant market usage, and more and more developers are being called upon to develop big data solutions using the Hadoop framework. This book covers the process from beginning to end, providing a crash course for professionals needing to learn and apply Hadoop quickly. Configure storage, UE, and in-memory computing Integrate Hadoop with other programs including Kafka and Storm Master the fundamentals of Apache Big Top and Ignite Build robust data security with expert tips and advice Hadoop's popularity is largely due to its accessibility. Open-source and written in Java, the framework offers almost no barrier to entry for experienced database developers already familiar with the skills and requirements real-world programming entails. Professional Hadoop gives you the practical information and framework-specific skills you need quickly.

How can event streams help make your application more scalable, reliable, and maintainable? In this report, O’Reilly author Martin Kleppmann shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn’t new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age. Using several case studies, Kleppmann explains how these projects can help you reorient your database architecture around streams and materialized views. The benefits of this approach include better data quality, faster queries through precomputed caches, and real-time user interfaces. Learn how to open up your data for richer analysis and make your applications more scalable and robust in the face of failures. Understand stream processing fundamentals and their similarities to event sourcing, CQRS, and complex event processing Learn how logs can make search indexes and caches easier to maintain Explore the integration of databases with event streams, using the new Bottled Water open source tool Turn your database architecture inside out by orienting it around streams and materialized views

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.

This book is a step-by-step guide for learning how to use Spark for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, MLlib, and Spark ML. Big Data Analytics with Spark shows you how to use Spark and leverage its easy-to-use features to increase your productivity. You learn to perform fast data analysis using its in-memory caching and advanced execution engine, employ in-memory computing capabilities for building high-performance machine learning and low-latency interactive analytics applications, and much more. Moreover, the book shows you how to use Spark as a single integrated platform for a variety of data processing tasks, including ETL pipelines, BI, live data stream processing, graph analytics, and machine learning. The book also includes a chapter on Scala, the hottest functional programming language, and the language that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Kafka, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.

Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere About the Technology About the Book It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available. Readers need to know a programming language like Java and have basic familiarity with Hadoop. What's Inside Thoroughly updated for Hadoop 2 How to write YARN applications Integrate real-time technologies like Storm, Impala, and Spark Predictive analytics using Mahout and RR About the Reader About the Author Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects. Quotes Very insightful. A deep dive into the Hadoop world. - Andrea Tarocchi, Red Hat, Inc. The most complete material on Hadoop and its ecosystem known to mankind! - Arthur Zubarev, Vital Insights Clear and concise, full of insights and highly applicable information. - Edward de Oliveira Ribeiro, DataStax, Inc. Comprehensive up-to-date coverage of Hadoop 2. - Muthusamy Manigandan, OzoneMedia