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פֿ free ᅺ Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series) ᐗ Book Author 3usednewfrom ᖙ

פֿ free ᅺ Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series)  ᐗ Book Author 3usednewfrom ᖙ פֿ free ᅺ Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series) ᐗ Book Author 3usednewfrom ᖙ Solve Data Analytics Problems with Spark, PySpark, and Related Open Source Tools Spark is at the heart of todays Big Data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks In this guide, Big Data expert Jeffrey Aven covers all you need to know to leverage Spark, together with its extensions, subprojects, and wider ecosystem Aven combines a language agnostic introduction to foundational Spark concepts with extensive programming examples utilizing the popular and intuitive PySpark development environment This guides focus on Python makes it widely accessible to large audiences of data professionals, analysts, and developerseven those with little Hadoop or Spark experience Avens broad coverage ranges from basic to advanced Spark programming, and Spark SQL to machine learning Youll learn how to efficiently manage all forms of data with Spark streaming, structured, semi structured, and unstructured Throughout, concise topic overviews quickly get you up to speed, and extensive hands on exercises prepare you to solve real problems Coverage includes Understand Sparks evolving role in the Big Data and Hadoop ecosystems Create Spark clusters using various deployment modes Control and optimize the operation of Spark clusters and applications Master Spark Core RDD API programming techniques Extend, accelerate, and optimize Spark routines with advanced API platform constructs, including shared variables, RDD storage, and partitioning Efficiently integrate Spark with both SQL and nonrelational data stores Perform stream processing and messaging with Spark Streaming and Apache Kafka Implement predictive modeling with SparkR and Spark MLlib Data analysis Wikipedia Data is a process of inspecting, cleansing, transforming, and modeling data with the goal discovering useful information, informing conclusions, supporting decision making has multiple facets approaches, encompassing diverse techniques under variety names, while being used in different business, Big Analytics IBM Analytics A lake shared environment that comprises repositories capitalizes on big technologies It provides to an organization for analytics processes Analytics discovery, interpretation, communication meaningful patterns dataEspecially valuable areas rich recorded relies simultaneous application statistics, computer programming operations research quantify performance Organizations may apply business IBM Cloud Private Organize Create trusted foundation all your into trusted, ready built governance, protection compliance controls Visualization Software TIBCO Spotfire leading independent provider infrastructure software creating event enabled enterprises use premise or as part cloud computing environments Machine Learning, Science, Big Data, Analytics, AI More Recent Stories Analytica Sr Scientist Washington, DC UC Santa Barbara Computational Linguist Assistant Professor SparkPost Science Engineer Columbia, MD San Francisc Safeguarding Help Google Support First party Cookies collects first cookies, related device browser, IP address site app activities measure report statistics about user interactions websites apps Reports wcationate Search our MDE Center Gartner Summit Orlando, Florida In this world ambiguity characterized by uncertainty, risk, doubt fake news, now time lead purpose bring clarity through you can rely and, most importantly, trust Use advanced tools get deeper understanding customers so deliver better experiences Studio Unlock insights from engaging, customizable reports Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series)

 

    • Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series)
    • 1.3
    • 58
    • Kindle
    • 320 pages
    • 013484601X
    • 3usednewfrom
    • English
    • 13 January 2017

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