Process large volumes of data in real-time while building high performance and robust data stream processing pipeline using the latest Apache Kafka 2.0 Key FeaturesSolve practical large data and processing challenges with KafkaTackle data processing challenges like late events, windowing, and watermarkingUnderstand real-time streaming applications processing using Schema registry, Kafka connect, Kafka streams, and KSQLBook Description Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the fly. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows. What you will learnHow to validate data with KafkaAdd information to existing data flowsGenerate new information through message compositionPerform data validation and versioning with the Schema RegistryHow to perform message Serialization and DeserializationHow to perform message Serialization and DeserializationProcess data streams with Kafka StreamsUnderstand the duality between tables and streams with KSQLWho this book is for This book is for developers who want to quickly master the practical concepts behind Apache Kafka. The audience need not have come across Apache Kafka previously; however, a familiarity of Java or any JVM language will be helpful in understanding the code in this book.

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.

Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key FeaturesLearn about the core concepts and the latest developments in Apache SparkMaster writing efficient big data applications with Spark’s built-in modules for SQL, Streaming, Machine Learning and Graph analysisGet introduced to a variety of optimizations based on the actual experienceBook Description Apache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications. What you will learnLearn core concepts such as RDDs, DataFrames, transformations, and moreSet up a Spark development environmentChoose the right APIs for your applicationsUnderstand Spark’s architecture and the execution flow of a Spark applicationExplore built-in modules for SQL, streaming, ML, and graph analysisOptimize your Spark job for better performanceWho this book is for If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.

Handle every problem you come across in the world of Clojure programming with this expert collection of recipes About This Book Discover a wide variety of practical cases and real world techniques to enhance your productivity with Clojure. Learn to resolve the everyday issues you face with a functional mindset using Clojure You will learn to write highly efficient, more productive, and error-free programs without the risk of deadlocks and race-conditions Who This Book Is For This book is for Clojure developers who have some Clojure programming experience and are well aware of their shortcomings. If you want to learn to tackle common problems, become an expert, and develop a solid skill set, then this book is for you. What You Will Learn Manipulate, access, filter, and transform your data with Clojure Write efficient parallelized code through Clojure abstractions Tackle Complex Concurrency easily with Reactive Programming Build on Haskell abstractions to write dynamic functional tests Write AWS Lambda functions effortlessly Put Clojure in use into your IoT devices Use Clojure with Slack for instant monitoring Scaling your Clojure application using Docker Develop real-time system interactions using MQTT and websockets In Detail When it comes to learning and using a new language you need an effective guide to be by your side when things get rough. For Clojure developers, these recipes have everything you need to take on everything this language offers. This book is divided into three high impact sections. The first section gives you an introduction to live programming and best practices. We show you how to interact with your connections by manipulating, transforming, and merging collections. You'll learn how to work with macros, protocols, multi-methods, and transducers. We'll also teach you how to work with languages such as Java, and Scala. The next section deals with intermediate-level content and enhances your Clojure skills, here we'll teach you concurrency programming with Clojure for high performance. We will provide you with advanced best practices, tips on Clojure programming, and show you how to work with Clojure while developing applications. In the final section you will learn how to test, deploy and analyze websocket behavior when your app is deployed in the cloud. Finally, we will take you through DevOps. Developing with Clojure has never been easier with these recipes by your side! Style and approach This book takes a recipe-based approach by diving directly into helpful programming concepts. It will give you a foolproof approach to programming and teach you how to deal with problems that may arise while working with Clojure. The book is divided into three sections giving you the freedom skip to the section of your choice depending on the problem faced.

Explore IoT Architecture, Design, and its Implementation KEY FEATURES ● Comprehensive overview of frameworks, protocols, networks, security, and privacy of IoT. ● Covers innovative IoT use cases and industry-wide application areas. ● Includes case studies to demonstrate IoT principles and practices. DESCRIPTION Internet of Things (IoT) A Quick Start Guide explains the architecture, design, and implementation of IoT. The book charts a path where none exists and introduces readers to the ethical and responsible development of IoT solutions. The book begins with the history of IoT, followed by chapters on architectures, networks, and protocols in both software and hardware. The book reveals the next level of IoT framework knowledge, such as ThingWorx and Salesforce Thunder. This book places equal emphasis on a wide range of security and privacy aspects, including Zero Trust Approaches, Forensics, Access Control Lists, and Public Key Infrastructure. Wearables, Industry 4.0, Workplace Analytics, and Product Asset Management are just a few of the applications and use cases that are discussed. Transformative trends such as Augmented Analytics, AR/VR, Digital Twins, and many more are also discussed in the book. After reading this book, readers will get a broad spectrum of knowledge of IoT. They will be able to put the guidance shared to use. WHAT YOU WILL LEARN ● Access to a variety of IoT application areas with compelling use cases. ● Opportunity to experiment with frameworks, tools, and platforms for various IoT assignments. ● Acquire conceptual knowledge about IoT architecture, protocols, and networks. ● Take a look at integrating IoT procedures, software, and hardware. ● Investigate how to develop a data management strategy when implementing IoT. ● Understand the policies governing IoT security, privacy, and interoperability. WHO THIS BOOK IS FOR This book is intended for IT graduates, computer engineers, and industry experts who wish to learn IoT principles, techniques, and protocols to successfully create and deploy safe and secure IoT systems. One does not need prior knowledge of IoT or programming to read this book. TABLE OF CONTENTS 1. IoT: The Basic Dynamics 2. IoT—Nuts and Bolts of the Architecture 3. Data Management Strategy 4. IoT Security, Privacy and Interoperability: What, Why, How, and What Next 5. Applications and Use Cases 6. Current and Future Trends

Get up and running with Oracle’s premium cloud blockchain services and build distributed blockchain apps with ease Key FeaturesDiscover Hyperledger Fabric and its components, features, qualifiers, and architectureGet familiar with the Oracle Blockchain Platform and its unique featuresBuild Hyperledger Fabric-based business networks with Oracle’s premium blockchain cloud serviceBook Description Hyperledger Fabric empowers enterprises to scale out in an unprecedented way, allowing organizations to build and manage blockchain business networks. This quick start guide systematically takes you through distributed ledger technology, blockchain, and Hyperledger Fabric while also helping you understand the significance of Blockchain-as-a-Service (BaaS). The book starts by explaining the blockchain and Hyperledger Fabric architectures. You'll then get to grips with the comprehensive five-step design strategy - explore, engage, experiment, experience, and influence. Next, you'll cover permissioned distributed autonomous organizations (pDAOs), along with the equation to quantify a blockchain solution for a given use case. As you progress, you'll learn how to model your blockchain business network by defining its assets, participants, transactions, and permissions with the help of examples. In the concluding chapters, you'll build on your knowledge as you explore Oracle Blockchain Platform (OBP) in depth and learn how to translate network topology on OBP. By the end of this book, you will be well-versed with OBP and have developed the skills required for infrastructure setup, access control, adding chaincode to a business network, and exposing chaincode to a DApp using REST configuration. What you will learnModel your blockchain-based business network by defining its components, transactions, integrations, and infrastructure through use casesDevelop, deploy, and test chaincode using shim and REST, and integrate it with client apps using SDK, REST, and eventsExplore accounting, blockchain, hyperledger fabric, and its components, features, qualifiers, architecture and structureUnderstand the importance of Blockchain-as-a-Service (BaaS)Experiment Hyperledger Fabric and delve into the underlying technologySet up a consortium network, nodes, channels, and privacy, and learn how to translate network topology on OBPWho this book is for If you are a blockchain developer, blockchain architect or just a cloud developer looking to get hands-on with Oracle Blockchain Cloud Service, then this book is for you. Some familiarity with the basic concepts of blockchain will be helpful to get the most out of this book

Get productive quickly with Pentaho Data Integration Key Features Take away the pain of starting with a complex and powerful system Simplify your data transformation and integration work Explore, transform, and validate your data with Pentaho Data Integration Book Description Pentaho Data Integration(PDI) is an intuitive and graphical environment packed with drag and drop design and powerful Extract-Transform-Load (ETL) capabilities. Given its power and flexibility, initial attempts to use the Pentaho Data Integration tool can be difficult or confusing. This book is the ideal solution. This book reduces your learning curve with PDI. It provides the guidance needed to make you productive, covering the main features of Pentaho Data Integration. It demonstrates the interactive features of the graphical designer, and takes you through the main ETL capabilities that the tool offers. By the end of the book, you will be able to use PDI for extracting, transforming, and loading the types of data you encounter on a daily basis. What you will learn Design, preview and run transformations in Spoon Run transformations using the Pan utility Understand how to obtain data from different types of files Connect to a database and explore it using the database explorer Understand how to transform data in a variety of ways Understand how to insert data into database tables Design and run jobs for sequencing tasks and sending emails Combine the execution of jobs and transformations Who this book is for This book is for software developers, business intelligence analysts, and others involved or interested in developing ETL solutions, or more generally, doing any kind of data manipulation.

Summary 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 Readers need to know a programming language like Java and have basic familiarity with Hadoop. 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. Table of Contents PART 1 BACKGROUND AND FUNDAMENTALS Hadoop in a heartbeat Introduction to YARN PART 2 DATA LOGISTICS Data serialization—working with text and beyond Organizing and optimizing data in HDFS Moving data into and out of Hadoop PART 3 BIG DATA PATTERNS Applying MapReduce patterns to big data Utilizing data structures and algorithms at scale Tuning, debugging, and testing PART 4 BEYOND MAPREDUCE SQL on Hadoop Writing a YARN application

Master the wicked-fast Apache Kafka streaming platform through hands-on examples and real-world projects. In Kafka in Action you will learn: Understanding Apache Kafka concepts Setting up and executing basic ETL tasks using Kafka Connect Using Kafka as part of a large data project team Performing administrative tasks Producing and consuming event streams Working with Kafka from Java applications Implementing Kafka as a message queue Kafka in Action is a fast-paced introduction to every aspect of working with Apache Kafka. Starting with an overview of Kafka's core concepts, you'll immediately learn how to set up and execute basic data movement tasks and how to produce and consume streams of events. Advancing quickly, you’ll soon be ready to use Kafka in your day-to-day workflow, and start digging into even more advanced Kafka topics. About the technology Think of Apache Kafka as a high performance software bus that facilitates event streaming, logging, analytics, and other data pipeline tasks. With Kafka, you can easily build features like operational data monitoring and large-scale event processing into both large and small-scale applications. About the book Kafka in Action introduces the core features of Kafka, along with relevant examples of how to use it in real applications. In it, you’ll explore the most common use cases such as logging and managing streaming data. When you’re done, you’ll be ready to handle both basic developer- and admin-based tasks in a Kafka-focused team. What's inside Kafka as an event streaming platform Kafka producers and consumers from Java applications Kafka as part of a large data project About the reader For intermediate Java developers or data engineers. No prior knowledge of Kafka required. About the author Dylan Scott is a software developer in the insurance industry. Viktor Gamov is a Kafka-focused developer advocate. At Confluent, Dave Klein helps developers, teams, and enterprises harness the power of event streaming with Apache Kafka. Table of Contents PART 1 GETTING STARTED 1 Introduction to Kafka 2 Getting to know Kafka PART 2 APPLYING KAFK 3 Designing a Kafka project 4 Producers: Sourcing data 5 Consumers: Unlocking data 6 Brokers 7 Topics and partitions 8 Kafka storage 9 Management: Tools and logging PART 3 GOING FURTHER 10 Protecting Kafka 11 Schema registry 12 Stream processing with Kafka Streams and ksqlDB