Apache Ignite is one of the most widely used open source memory-centric distributed, caching, and processing platform. This allows the users to use the platform as an in-memory computing framework or a full functional persistence data stores with SQL and ACID transaction support. On the other hand, Apache Ignite can be used for accelerating existing Relational and NoSQL databases, processing events & streaming data or developing Microservices in fault-tolerant fashion. This book addressed anyone interested in learning in-memory computing and distributed database. This book intends to provide someone with little to no experience of Apache Ignite with an opportunity to learn how to use this platform effectively from scratch taking a practical hands-on approach to learning. Please see the table of contents for more details.

Build efficient, high-performance & scalable systems to process large volumes of data with Apache Ignite Key FeaturesUnderstand Apache Ignite's in-memory technologyCreate High-Performance app components with IgniteBuild a real-time data streaming and complex event processing systemBook Description Apache Ignite is a distributed in-memory platform designed to scale and process large volume of data. It can be integrated with microservices as well as monolithic systems, and can be used as a scalable, highly available and performant deployment platform for microservices. This book will teach you to use Apache Ignite for building a high-performance, scalable, highly available system architecture with data integrity. The book takes you through the basics of Apache Ignite and in-memory technologies. You will learn about installation and clustering Ignite nodes, caching topologies, and various caching strategies, such as cache aside, read and write through, and write behind. Next, you will delve into detailed aspects of Ignite’s data grid: web session clustering and querying data. You will learn how to process large volumes of data using compute grid and Ignite’s map-reduce and executor service. You will learn about the memory architecture of Apache Ignite and monitoring memory and caches. You will use Ignite for complex event processing, event streaming, and the time-series predictions of opportunities and threats. Additionally, you will go through off-heap and on-heap caching, swapping, and native and Spring framework integration with Apache Ignite. By the end of this book, you will be confident with all the features of Apache Ignite 2.x that can be used to build a high-performance system architecture. What you will learnUse Apache Ignite’s data grid and implement web session clusteringGain high performance and linear scalability with in-memory distributed data processingCreate a microservice on top of Apache Ignite that can scale and performPerform ACID-compliant CRUD operations on an Ignite cacheRetrieve data from Apache Ignite’s data grid using SQL, Scan and Lucene Text queryExplore complex event processing concepts and event streamingIntegrate your Ignite app with the Spring frameworkWho this book is for The book is for Big Data professionals who want to learn the essentials of Apache Ignite. Prior experience in Java is necessary.

In February 1861, the twelve-year-old son of Arizona rancher John Ward was kidnapped by Apaches. What followed would ignite a Southwestern frontier war between the Chiricahuas and the US Army that would last twenty-five years. In the days following the initial melee, innocent passersby would be taken as hostages on both sides, and almost all of them would be brutally slaughtered. Thousands of lives would be lost, the economies of Arizona and New Mexico would be devastated, and in the end, the Chiricahua way of life would essentially cease to exist. In a gripping narrative that often reads like an old-fashioned Western novel, Terry Mort explores the collision of these two radically different cultures in a masterful account of one of the bloodiest conflicts in our frontier history.

Designing and writing a real-time streaming publication with Apache Apex About This Book Get a clear, practical approach to real-time data processing Program Apache Apex streaming applications This book shows you Apex integration with the open source Big Data ecosystem Who This Book Is For This book assumes knowledge of application development with Java and familiarity with distributed systems. Familiarity with other real-time streaming frameworks is not required, but some practical experience with other big data processing utilities might be helpful. What You Will Learn Put together a functioning Apex application from scratch Scale an Apex application and configure it for optimal performance Understand how to deal with failures via the fault tolerance features of the platform Use Apex via other frameworks such as Beam Understand the DevOps implications of deploying Apex In Detail Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it. Style and approach This book is divided into two major parts: first it explains what Apex is, what its relevant parts are, and how to write well-built Apex applications. The second part is entirely application-driven, walking you through Apex applications of increasing complexity.

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

In the early morning of November 29, 1864, with the fate of the Union still uncertain, part of the First Colorado and nearly all of the Third Colorado volunteer regiments, commanded by Colonel John Chivington, surprised hundreds of Cheyenne and Arapaho people camped on the banks of Sand Creek in southeastern Colorado Territory. More than 150 Native Americans were slaughtered, the vast majority of them women, children, and the elderly, making it one of the most infamous cases of state-sponsored violence in U.S. history. A Misplaced Massacre examines the ways in which generations of Americans have struggled to come to terms with the meaning of both the attack and its aftermath, most publicly at the 2007 opening of the Sand Creek Massacre National Historic Site. This site opened after a long and remarkably contentious planning process. Native Americans, Colorado ranchers, scholars, Park Service employees, and politicians alternately argued and allied with one another around the question of whether the nation’s crimes, as well as its achievements, should be memorialized. Ari Kelman unearths the stories of those who lived through the atrocity, as well as those who grappled with its troubling legacy, to reveal how the intertwined histories of the conquest and colonization of the American West and the U.S. Civil War left enduring national scars. Combining painstaking research with storytelling worthy of a novel, A Misplaced Massacre probes the intersection of history and memory, laying bare the ways differing groups of Americans come to know a shared past.

Integrate open source data analytics and build business intelligence on SQL databases with Apache Superset. The quick, intuitive nature for data visualization in a web application makes it easy for creating interactive dashboards. Key FeaturesWork with Apache Superset's rich set of data visualizationsCreate interactive dashboards and data storytellingEasily explore dataBook Description Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset. First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe. You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data. Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers. What you will learnGet to grips with the fundamentals of data exploration using SupersetSet up a working instance of Superset on cloud services like Google Compute EngineIntegrate Superset with SQL databasesBuild dashboards with SupersetCalculate statistics in Superset for numerical, categorical, or text dataUnderstand visualization techniques, filtering, and grouping by aggregationManage user roles and permissions in SupersetWork with SQL LabWho this book is for This book is for data analysts, BI professionals, and developers who want to learn Apache Superset. If you want to create interactive dashboards from SQL databases, this book is what you need. Working knowledge of Python will be an advantage but not necessary to understand this book.