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Machine learning. --- Spark (Electronic resource : Apache Software Foundation).

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This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

Information systems --- Big data. --- Data mining --- Données volumineuses --- Computer programs. --- Spark (Electronic resource : Apache Software Foundation) --- SPARK (Electronic resource) --- SPARK (Electronic resource). --- Données volumineuses

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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.

Computer science. --- Data mining. --- Application software. --- Computer Science. --- Computer Appl. in Administrative Data Processing. --- Data Mining and Knowledge Discovery. --- Streaming technology (Telecommunications) --- Big data. --- Spark (Electronic resource : Apache Software Foundation) --- Data sets, Large --- Large data sets --- Streamed media --- Streaming media --- Streaming resources --- Apache Spark (Electronic resource : Apache Software Foundation) --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Data transmission systems --- Multimedia systems --- Data sets --- Information systems. --- Big Data.

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Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You'll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical adviceIntegrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and SparkUse StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processingUtilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processingTurbocharge Spark with Alluxio, a distributed in-memory storage platformDeploy big data in the cloud using Cloudera DirectorPerform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and SparkUnderstand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasksImplement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modelingStudy real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and MastercardWho This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

Information systems --- MySQL --- cloud computing --- IoT (Internet of Things) --- Apache (informatica) --- big data --- time series analysis --- gegevensanalyse --- SQL (structured query language) --- Computer science --- Big data, --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data sets, Large --- Large data sets --- Data sets --- Informatics --- Science --- Spark (Electronic resource : Apache Software Foundation) --- Computer science. --- Big data.

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Gain the key language concepts and programming techniques of Scala in the context of big data analytics and Apache Spark. The book begins by introducing you to Scala and establishes a firm contextual understanding of why you should learn this language, how it stands in comparison to Java, and how Scala is related to Apache Spark for big data analytics. Next, you’ll set up the Scala environment ready for examining your first Scala programs. You’ll start with code blocks that allow you to group and execute related statements together as a block and see the implications for Scala’s type system. The author discusses functions at length and highlights a number of associated concepts such as zero-parity functions, single-line functions, and anonymous functions. Along the way you’ll see the development life cycle of a Scala program. This involves compiling and building programs using the industry-standard Scala Build Tool (SBT). You’ll cover guidelines related to dependency management using SBT as this is critical for building large Apache Spark applications. Scala Programming for Big Data Analytics concludes by demonstrating how you can make use of the concepts to write programs that run on the Apache Spark framework. These programs will provide distributed and parallel computing, which is critical for big data analytics. You will: See the fundamentals of Scala as a general-purpose programming language Understand functional programming and object-oriented programming constructs in Scala Comprehend the use and various features of Scala REPL (shell) Use Scala collections and functions Employ functional programming constructs.

Scala (Computer program language) --- Data mining. --- Big data. --- Spark (Electronic resource : Apache Software Foundation) --- Computer science. --- Open source software. --- Computer programming. --- Big Data/Analytics. --- Programming Languages, Compilers, Interpreters. --- Open Source. --- Data sets, Large --- Large data sets --- Data sets --- Computers --- Electronic computer programming --- Electronic data processing --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Free software (Open source software) --- Open code software --- Opensource software --- Computer software --- Informatics --- Science --- Programming --- Programming languages (Electronic computers). --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Languages, Artificial