Practical Apache Spark : using the Scala API /: using the Scala API. ([2018])

• Record Type: Book
• Further Information: Note: Subhashini Chellappan and Dharanitharan Ganesan.
• Authors: Chellappan, Subhashini; Ganesan, Dharanitharan
• Contents: Intro; Table of Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: Scala: Functional Programming Aspects; What Is Functional Programming?; What Is a Pure Function?; Example of Pure Function; Scala Programming Features; Variable Declaration and Initialization; Type Inference; Immutability; Lazy Evaluation; String Interpolation; String - s Interpolator; String - f Interpolator; String - raw Interpolator; Pattern Matching; Scala Class vs. Object; Singleton Object; Companion Classes and Objects; Case Classes; Pattern Matching on Case Classes Scala CollectionsIterating Over the Collection; Common Methods of Collection; Functional Programming Aspects of Scala; Anonymous Functions; Higher Order Functions; Function Composition; Function Currying; Nested Functions; Functions with Variable Length Parameters; Reference Links; Points to Remember; Chapter 2: Single and Multinode Cluster Setup; Spark Multinode Cluster Setup; Recommended Platform; Operating System; Prerequisites; Spark Installation Steps; Spark Web UI; Spark Master UI; Spark Application UI; Stopping the Spark Cluster; Spark Single-Node Cluster Setup; Prerequisites Spark Installation StepsSpark Master UI; Points to Remember; Chapter 3: Introduction to Apache Spark and Spark Core; What Is Apache Spark?; Why Apache Spark?; Spark vs. Hadoop MapReduce; Apache Spark Architecture; Spark Components; Spark Core (RDD); Spark SQL; Spark Streaming; MLib; GraphX; SparkR; Spark Shell; Spark Core: RDD; RDD Operations; Transformations; Actions; Creating an RDD; Using Parallelized Collection; From External Data Source; Creating an RDD from the Hadoop File System; Creating an RDD: File Partitioning; RDD Transformations; RDD Actions; Working with Pair RDDs Direct Acylic Graph in Apache SparkHow DAG Works in Spark; How Spark Achieves Fault Tolerance Through DAG; Persisting RDD; Shared Variables; Broadcast Variables; Accumulators; Simple Build Tool (SBT); Assignments; Reference Links; Points to Remember; Chapter 4: Spark SQL, DataFrames, and Datasets; What Is Spark SQL?; Datasets and DataFrames; Spark Session; Creating DataFrames; DataFrame Operations; Untyped DataFrame Operation: Select; Untyped DataFrame Operation: Filter; Untyped DataFrame Operation: Aggregate Operations; Running SQL Queries Programatically; Creating Views; Dataset Operations Interoperating with RDDsReflection-Based Approach to Infer Schema; Different Data Sources; Generic Load and Save Functions; Manually Specifying Options; Run SQL on Files Directly; JDBC to External Databases; Working with Hive Tables; Building Spark SQL Application with SBT; Points to Remember; Chapter 5: Introduction to Spark Streaming; Data Processing; Streaming Data; Why Streaming Data Are Important; Introduction to Spark Streaming; Internal Working of Spark Streaming; Spark Streaming Concepts; Discretized Streams (DStream); Streaming Context; DStream Operations
• Publisher Details: Place of publication not identified : Apress
• Publication Date: 2018
• Copyright Date: 2018
• Extent: 1 online resource
• Subjects: 005.758; Scala (Computer program language); COMPUTERS / Databases / General; Scala (Computer program language); Electronic books
• Languages: English
• ISBNs: 9781484236529; 1484236521
• Related ISBNs: 9781484236512
• Notes: Note: Vendor-supplied metadata.
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