Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data. About the Technology GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets. GraphX gives you unprecedented speed and capacity for running massively parallel and machine learning algorithms. About the Book Spark GraphX in Action begins with the big picture of what graphs can be used for. This example-based tutorial teaches you how to use GraphX interactively. You'll start with a crystal-clear introduction to building big data graphs from regular data, and then explore the problems and possibilities of implementing graph algorithms and architecting graph processing pipelines. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data. What's Inside Understanding graph technology Using the GraphX API Developing algorithms for big graphs Machine learning with graphs Graph visualization About the Reader Readers should be comfortable writing code. Experience with Apache Spark and Scala is not required. About the Authors Michael Malak has worked on Spark applications for Fortune 500 companies since early 2013. Robin East has worked as a consultant to large organizations for over 15 years and is a data scientist at Worldpay. Quotes Learn complex graph processing from two experienced authors…A comprehensive guide. - Gaurav Bhardwaj, 3Pillar Global The best resource to go from GraphX novice to expert in the least amount of time. - Justin Fister, PaperRater A must-read for anyone serious about large-scale graph data mining! - Antonio Magnaghi, OpenMail Reveals the awesome and elegant capabilities of working with linked data for large-scale datasets. - Sumit Pal, Independent consultant

Learn how to configure, implement, enhance, and customize SAP OEE to address manufacturing performance management. Manufacturing Performance Management using SAP OEE will show you how to connect your business processes with your plant systems and how to integrate SAP OEE with ERP through standard workflows and shop floor systems for automated data collection. Manufacturing Performance Management using SAP OEE is a must-have comprehensive guide to implementing SAP OEE. It will ensure that SAP consultants and users understand how SAP OEE can offer solutions for manufacturing performance management in process industries. With this book in hand, managing shop floor execution effectively will become easier than ever. Authors Dipankar Saha and Mahalakshmi Symsunder, both SAP manufacturing solution experts, and Sumanta Chakraborty, product owner of SAP OEE, will explain execution and processing related concepts, manual and automatic data collection through the OEE Worker UI, and how to enhance and customize interfaces and dashboards for your specific purposes. You'll learn how to capture and categorize production and loss data and use it effectively for root-cause analysis. In addition, this book will show you: Various down-time handling scenarios. How to monitor, calculate, and define standard as well as industry-specific KPIs. How to carry out standard operational analytics for continuous improvement on the shop floor, at local plant level using MII and SAP Lumira, and also global consolidated analytics at corporation level using SAP HANA. Steps to benchmark manufacturing performance to compare similar manufacturing plants' performance, leading to a more efficient and effective shop floor. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. What You Will Learn Configure your ERP OEE add-on to build your plant and global hierarchy and relevant master data and KPIs Use the SAP OEE standard integration (SAP OEEINT) to integrate your ECC and OEE system to establish bi-directional integration between the enterprise and the shop floor Enable your shop floor operator on the OEE Worker UI to handle shop floor production execution Use SAP OEE as a tool for measuring manufacturing performance Enhance and customize SAP OEE to suit your specific requirements Create local plant-based reporting using SAP Lumira and MII Use standard SAP OEE HANA analytics Who This Book Is For SAP MII, ME, and OEE consultants and users who will implement and use the solution.

This IBM® Redbooks® publication addresses topics to use the virtualization strengths of the IBM POWER8® platform to solve clients' system resource utilization challenges and maximize systems' throughput and capacity. This book addresses performance tuning topics that will help answer clients' complex analytic workload requirements, help maximize systems' resources, and provide expert-level documentation to transfer the how-to-skills to the worldwide teams. This book strengthens the position of IBM Analytics and Big Data solutions with a well-defined and documented deployment model within a POWER8 virtualized environment, offering clients a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted toward technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing analytics solutions and support on IBM Power Systems™.

In 'Apache Spark Machine Learning Blueprints', you'll explore how to create sophisticated and scalable machine learning projects using Apache Spark. This project-driven guide covers practical applications including fraud detection, customer analysis, and recommendation engines, helping you leverage Spark's capabilities for advanced data science tasks. What this Book will help me do Learn to set up Apache Spark efficiently for machine learning projects, unlocking its powerful processing capabilities. Integrate Apache Spark with R for detailed analytical insights, empowering your decision-making processes. Create predictive models for use cases including customer scoring, fraud detection, and risk assessment with practical implementations. Understand and utilize Spark's parallel computing architecture for large-scale machine learning tasks. Develop and refine recommendation systems capable of handling large user bases and datasets using Spark. Author(s) Alex Liu is a seasoned data scientist and software developer specializing in machine learning and big data technology. With extensive experience in using Apache Spark for predictive analytics, Alex has successfully built and deployed scalable solutions across industries. Their teaching approach combines theory and practical insights, making cutting-edge technologies accessible and actionable. Who is it for? This book is ideal for data analysts, data scientists, and developers with a foundation in machine learning who are eager to apply their knowledge in big data contexts. If you have a basic familiarity with Apache Spark and its ecosystem, and you're looking to enhance your ability to build machine learning applications, this resource is for you. It's particularly valuable for those aiming to utilize Spark for extensive data operations and gain practical, project-based insights.

Digital business has been driving the transformation of underlying IT infrastructure to be more efficient, secure, adaptive, and integrated. Information Technology (IT) must be able to handle the explosive growth of mobile clients and employees. IT also must be able to use enormous amounts of data to provide deep and real-time insights to help achieve the greatest business impact. This IBM® Redbooks® publication addresses the IBM Mainframe, the IBM z13™. The IBM z13 is the trusted enterprise platform for integrating data, transactions, and insight. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It needs to be an integrated infrastructure that can support new applications. It needs to have integrated capabilities that can provide new mobile capabilities with real-time analytics delivered by a secure cloud infrastructure. IBM z13 is designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows the z13 to deliver a record level of capacity over the prior IBM z Systems™. In its maximum configuration, z13 is powered by up to 141 client characterizable microprocessors (cores) running at 5 GHz. This configuration can run more than 110,000 millions of instructions per second (MIPS) and up to 10 TB of client memory. The IBM z13 Model NE1 is estimated to provide up to 40% more total system capacity than the IBM zEnterprise® EC12 (zEC1) Model HA1. This book provides information about the IBM z13 and its functions, features, and associated software support. Greater detail is offered in areas relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM z Systems functions and plan for their usage. It is not intended as an introduction to mainframes. Readers are expected to be generally familiar with existing IBM z Systems technology and terminology.

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 best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

Apache Hive Cookbook is a comprehensive resource for mastering Apache Hive, a tool that bridges the gap between SQL and Big Data processing. Through guided recipes, you'll acquire essential skills in Hive query development, optimization, and integration with modern big data frameworks. What this Book will help me do Design efficient Hive query structures for big data analytics. Optimize data storage and query execution using partitions and buckets. Integrate Hive seamlessly with frameworks like Spark and Hadoop. Understand and utilize the HiveQL syntax to perform advanced analytical processing. Implement practical solutions to secure, maintain, and scale Hive environments. Author(s) Hanish Bansal, Saurabh Chauhan, and Shrey Mehrotra bring their extensive expertise in big data technologies and Hive to this cookbook. With years of practical experience and deep technical knowledge, they offer a collection of solutions and best practices that reflect real-world use cases. Their commitment to clarity and depth makes this book an invaluable resource for exploring Hive to its fullest potential. Who is it for? This book is perfect for data professionals, engineers, and developers looking to enhance their capabilities in big data analytics using Hive. It caters to those with a foundational understanding of big data frameworks and some familiarity with SQL. Whether you're planning to optimize data handling or integrate Hive with other data tools, this guide helps you achieve your goals. Step into the world of efficient data analytics with Apache Hive through structured learning paths.

Data is the currency of the new economy, and organizations are increasingly tasked with finding better ways to protect, recover, access, share, and use data. Traditional storage technologies are being stretched to the breaking point. This challenge is not because of storage hardware performance, but because management tools and techniques have not kept pace with new requirements. Primary data growth rates of 35% to 50% annually only amplify the problem. Organizations of all sizes find themselves needing to modernize their IT processes to enable critical new use cases such as storage self-service, Development and Operations (DevOps), and integration of data centers with the Cloud. They are equally challenged with improving management efficiencies for long established IT processes such as data protection, disaster recovery, reporting, and business analytics. Access to copies of data is the one common feature of all these use cases. However, the slow, manual processes common to IT organizations, including a heavy reliance on labor-intensive scripting and disparate tool sets, are no longer able to deliver the speed and agility required in today's fast-paced world. Copy Data Management (CDM) is an IT modernization technology that focuses on using existing data in a manner that is efficient, automated, scalable, and easy to use, delivering the data access that is urgently needed to meet the new use cases. Catalogic ECX, with IBM® storage, provides in-place copy data management that modernizes IT processes, enables key use cases, and does it all within existing infrastructure. This IBM Redbooks® publication shows how Catalogic Software and IBM have partnered together to create an integrated solution that addresses today's IT environment.

Master the full potential of big data processing using Hadoop with this comprehensive guide. Featuring over 90 practical recipes, this book helps you streamline data workflows and implement machine learning models with tools like Spark, Hive, and Pig. By the end, you'll confidently handle complex data problems and optimize big data solutions effectively. What this Book will help me do Install and manage a Hadoop 2.x cluster efficiently to suit your data processing needs. Explore and utilize advanced tools like Hive, Pig, and Flume for seamless big data analysis. Master data import/export processes with Sqoop and workflows automation using Oozie. Implement machine learning and analytics tasks using Mahout and Apache Spark. Store and process data flexibly across formats like Parquet, ORC, RC, and more. Author(s) None Deshpande is an expert in big data processing and analytics with years of hands-on experience in implementing Hadoop-based solutions for real-world problems. Known for a clear and pragmatic writing style, None brings actionable wisdom and best practices to the forefront, helping readers excel in managing and utilizing big data systems. Who is it for? Designed for technical enthusiasts and professionals, this book is ideal for those familiar with basic big data concepts. If you are looking to expand your expertise in Hadoop's ecosystem and implement data-driven solutions, this book will guide you through essential skills and advanced techniques to efficiently manage complex big data projects.

GET MORE WITH MANNING An eBook copy of the previous edition, MongoDB in Action (First Edition), is included at no additional cost. It will be automatically added to your Manning Bookshelf within 24 hours of purchase. MongoDB in Action, Second Edition is a completely revised and updated version. It introduces MongoDB 3.0 and the document-oriented database model. This perfectly paced book gives you both the big picture you'll need as a developer and enough low-level detail to satisfy system engineers. About the Technology This document-oriented database was built for high availability, supports rich, dynamic schemas, and lets you easily distribute data across multiple servers. MongoDB 3.0 is flexible, scalable, and very fast, even with big data loads. About the Book MongoDB in Action, Second Edition is a completely revised and updated version. It introduces MongoDB 3.0 and the document-oriented database model. This perfectly paced book gives you both the big picture you'll need as a developer and enough low-level detail to satisfy system engineers. Lots of examples will help you develop confidence in the crucial area of data modeling. You'll also love the deep explanations of each feature, including replication, auto-sharding, and deployment. What's Inside Indexes, queries, and standard DB operations Aggregation and text searching Map-reduce for custom aggregations and reporting Deploying for scale and high availability Updated for Mongo 3.0 About the Reader Written for developers. No previous MongoDB or NoSQL experience is assumed. About the Authors After working at MongoDB, Kyle Banker is now at a startup. Peter Bakkum is a developer with MongoDB expertise. Shaun Verch has worked on the core server team at MongoDB. A Genentech engineer, Doug Garrett is one of the winners of the MongoDB Innovation Award for Analytics. A software architect, Tim Hawkins has led search engineering at Yahoo Europe. Technical Contributor: Wouter Thielen Technical Editor: Mihalis Tsoukalos Quotes A thorough manual for learning, practicing, and implementing MongoDB - Jeet Marwah, Acer Inc. A must-read to properly use MongoDB and model your data in the best possible way. - Hernan Garcia, Betterez Inc. Provides all the necessary details to get you jump-started with MongoDB. - Gregor Zurowski, Independent Software Development Consultant Awesome! MongoDB in a nutshell. - Hardy Ferentschik, Red Hat

Hadoop has revolutionized data processing and enterprise data warehousing, but its explosive growth has come with a large amount of uncertainty, hype, and confusion. With this report, enterprise decision makers will receive a concise crash course on what Hadoop is and why it’s important. Hadoop represents a major shift from traditional enterprise data warehousing and data analytics, and its technology can be daunting at first. Donald Miner, founder of the data science firm Miner & Kasch, covers just enough ground so you can make intelligent decisions about Hadoop in your enterprise. By the end of this report, you’ll know the basics of technologies such as HDFS, MapReduce, and YARN, without becoming mired in the details. Not only will you learn the basics of how Hadoop works and why it’s such an important technology, you’ll get examples of how you should probably be using it.

Organizations today are swimming in data, but most of them manage to analyze only a fraction of what they collect. To help build a stronger data-driven culture, many organizations are adopting a new approach called self-service analytics. This O’Reilly report examines how this approach provides data access to more people across a company, allowing business users to work with data themselves and create their own customized analyses. The result? More eyes looking at more data in more ways. Along with the perceived benefits, author Sandra Swanson also delves into the potential pitfalls of self-service analytics: balancing greater data access with concerns about security, data governance, and siloed data stores. Read this report and gain insights from enterprise tech (Yahoo), government (the City of Chicago), and disruptive retail (Warby Parker and Talend). Learn how these organizations are handling self-service analytics in practice. Sandra Swanson is a Chicago-based writer who’s covered technology, science, and business for dozens of publications, including ScientificAmerican.com. Connect with her on Twitter (@saswanson) or at www.saswanson.com.

This IBM® Redbooks® publication introduces the latest IBM z Systems™ platforms, the IBM z13™ and IBM z13s. It includes information about the z Systems environment and how it can help integrate data, transactions, and insight for faster and more accurate business decisions. The z13 and z13s are state-of-the-art data and transaction systems that deliver advanced capabilities that are vital to modern IT infrastructures. These capabilities include: Accelerated data and transaction serving Integrated analytics Access to the API economy Agile development and operations Efficient, scalable, and secure cloud services End-to-end security for data and transactions This book explains how these systems use both new innovations and traditional z Systems strengths to satisfy growing demand for cloud, analytics, and mobile applications. With one of these z Systems platforms as the base, applications can run in a trusted, reliable, and secure environment that both improves operations and lessens business risk.

IBM® Spectrum Control (Spectrum Control), a member of the IBM Spectrum™ Family of products, is the next-generation data management solution for software-defined environments (SDEs). With support for block, file, object workloads, and software-defined storage and predictive analytics, and automated and advanced monitoring to identify proactively storage performance problems, Spectrum Control enables administrators to provide efficient management for heterogeneous storage environments. IBM Spectrum Control™ (formerly IBM Tivoli® Storage Productivity Center) delivers a complete set of functions to manage IBM Spectrum Virtualize™, IBM Spectrum Accelerate™, and IBM Spectrum Scale™ storage infrastructures, and traditional IBM and select third-party storage hardware systems. This IBM Redbooks® publication provides practical examples and use cases that can be deployed with IBM Spectrum Control Standard Edition, with an overview of IBM Spectrum Control Advanced Edition. This book complements the Spectrum Control IBM Knowledge Center, which is referenced for product details, and for installation and implementation details throughout this book. You can find this resource as the following website: IBM Spectrum Control Knowledge Center Also provided are descriptions and an architectural overview of the IBM Spectrum Family, highlighting Spectrum Control, as integrated into software-defined storage environments. This publication is intended for storage administrators, clients who are responsible for maintaining IT and business infrastructures, and anyone who wants to learn more about employing Spectrum Control and Spectrum Control Standard Edition.

This book delves into the techniques and tools essential for designing, processing, and analyzing complex datasets in real-time using advanced frameworks like Apache Spark, Storm, and Amazon Kinesis. By engaging with this thorough guide, you'll build proficiency in creating robust, efficient, and scalable real-time data processing architectures tailored to real-world scenarios. What this Book will help me do Learn the fundamentals of real-time data processing and how it differs from batch processing. Gain hands-on experience with Apache Storm for creating robust data-driven solutions. Develop real-world applications using Amazon Kinesis for cloud-based analytics. Perform complex data queries and transformations with Spark SQL and understand Spark RDDs. Master the Lambda Architecture to combine batch and real-time analytics effectively. Author(s) Shilpi Saxena is a renowned expert in big data technologies, holding extensive experience in real-time data analytics. With a career spanning years in the industry, Shilpi has provided innovative solutions for big data challenges in top-tier organizations. Her teaching approach emphasizes practical applicability, making her writings accessible and impactful for developers and architects alike. Who is it for? This book is for software professionals such as Big Data architects, developers, or programmers looking to enhance their skills in real-time big data analytics. If you are familiar with basic programming principles and seek to build solutions for processing large data streams in real-time environments, this book caters to your needs. It is also suitable for those seeking to familiarize themselves with using state-of-the-art tools like Spark SQL, Apache Storm, and Amazon Kinesis. Whether you're extending current expertise or transitioning into this field, this resource helps you achieve your objectives.

Dynamic organizations want to accelerate growth while reducing costs. To do so, they must speed the deployment of business applications and adapt quickly to any changes in priorities. Organizations require an IT infrastructure to be easy, efficient, and versatile. The VersaStack solution by Cisco and IBM® can help you accelerate the deployment of your datacenters. It reduces costs by more efficiently managing information and resources while maintaining your ability to adapt to business change. The VersaStack solution combines the innovation of Cisco Unified Computing System (Cisco UCS) Integrated Infrastructure with the efficiency of the IBM Storwize® storage system. The Cisco UCS Integrated Infrastructure includes the Cisco UCS, Cisco Nexus and Cisco MDS switches, and Cisco UCS Director. The IBM Storwize V7000 storage system enhances virtual environments with its Data Virtualization, IBM Real-time Compression™, and IBM Easy Tier® features. These features deliver extraordinary levels of performance and efficiency. The VersaStack solution is Cisco Application Centric Infrastructure (ACI) ready. Your IT team can build, deploy, secure, and maintain applications through a more agile framework. Cisco Intercloud Fabric capabilities help enable the creation of open and highly secure solutions for the hybrid cloud. These solutions accelerate your IT transformation while delivering dramatic improvements in operational efficiency and simplicity. Cisco and IBM are global leaders in the IT industry. The VersaStack solution gives you the opportunity to take advantage of integrated infrastructure solutions that are targeted at enterprise applications, analytics, and cloud solutions. The VersaStack solution is backed by Cisco Validated Designs (CVDs) to provide faster delivery of applications, greater IT efficiency, and less risk. This IBM Redbooks® publication is aimed at experienced storage administrators that are tasked with deploying a VersaStack solution with IBM DB2® High Availability (DB2 HA), IBM Spectrum™ Protect, and IBM Spectrum Control™.

"Elasticsearch Essentials" provides a comprehensive introduction to Elasticsearch, the powerful search and analytics engine. This book delivers a fast-paced, practical guide to harnessing Elasticsearch for creating scalable search and analytics applications. What this Book will help me do Learn to effectively use Elasticsearch REST APIs for search and analytics. Understand and design schema and mappings with best practices. Master data modeling concepts for efficient data queries. Develop skills to create and manage Elasticsearch clusters in production. Learn techniques for ensuring high availability and handling large datasets. Author(s) Bharvi Dixit is a seasoned developer and expert in search technologies with hands-on experience in Elasticsearch and other search solutions. With extensive knowledge in data analytics and large-scale systems, Bharvi ensures readers gain practical skills and insights through well-structured examples and explanations. Who is it for? This book is perfect for developers looking to enhance their skills in building search and analytics solutions with Elasticsearch. It's particularly suited for those familiar with search technologies like Apache Lucene or Solr but new to Elasticsearch. Beginners to intermediate learners in big data and analytics will find the structured approach beneficial. It's ideal for professionals aspiring to develop advanced search implementations with modern tools.

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.