Step-by-step instructions walk students through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help students avoid pitfalls. By the time readers finish, they'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.
Step-by-step instructions walk students through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help students avoid pitfalls. By the time readers finish, they'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jeffrey Aven is a big data consultant and instructor based in Melbourne, Australia. Jeff has an extensive background in data management and several years of experience consulting and teaching in the areas of Hadoop, HBase, Spark, and other big data ecosystem technologies. Jeff has won accolades as a big data instructor and is also an accomplished consultant who has been involved in several high-profile, enterprise-scale big data implementations across different industries in the region.
Inhaltsangabe
PART I: GETTING STARTED WITH APACHE SPARK Hour 1: Introducing Apache Spark Hour 2: Understanding Hadoop Hour 3: Installing Spark Hour 4: Understanding the Spark Application Architecture Hour 5: Deploying Spark in the Cloud PART II: PROGRAMMING WITH APACHE SPARK Hour 6: Learning the Basics of Spark Programming with RDDs Hour 7: Understanding MapReduce Concepts Hour 8: Getting Started with Scala Hour 9: Functional Programming with Python Hour 10: Working with the Spark API (Transformations and Actions) Hour 11: Using RDDs: Caching, Persistence, and Output Hour 12: Advanced Spark Programming PART III: EXTENSIONS TO SPARK Hour 13: Using SQL with Spark Hour 14: Stream Processing with Spark Hour 15: Getting Started with Spark and R Hour 16: Machine Learning with Spark Hour 17: Introducing Sparkling Water (H20 and Spark) Hour 18: Graph Processing with Spark Hour 19: Using Spark with NoSQL Systems Hour 20: Using Spark with Messaging Systems PART IV: MANAGING SPARK Hour 21: Administering Spark Hour 22: Monitoring Spark Hour 23: Extending and Securing Spark Hour 24: Improving Spark Performance Index
PART I: GETTING STARTED WITH APACHE SPARK Hour 1: Introducing Apache Spark Hour 2: Understanding Hadoop Hour 3: Installing Spark Hour 4: Understanding the Spark Application Architecture Hour 5: Deploying Spark in the Cloud PART II: PROGRAMMING WITH APACHE SPARK Hour 6: Learning the Basics of Spark Programming with RDDs Hour 7: Understanding MapReduce Concepts Hour 8: Getting Started with Scala Hour 9: Functional Programming with Python Hour 10: Working with the Spark API (Transformations and Actions) Hour 11: Using RDDs: Caching, Persistence, and Output Hour 12: Advanced Spark Programming PART III: EXTENSIONS TO SPARK Hour 13: Using SQL with Spark Hour 14: Stream Processing with Spark Hour 15: Getting Started with Spark and R Hour 16: Machine Learning with Spark Hour 17: Introducing Sparkling Water (H20 and Spark) Hour 18: Graph Processing with Spark Hour 19: Using Spark with NoSQL Systems Hour 20: Using Spark with Messaging Systems PART IV: MANAGING SPARK Hour 21: Administering Spark Hour 22: Monitoring Spark Hour 23: Extending and Securing Spark Hour 24: Improving Spark Performance Index
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