Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library, Hien Luu

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The instance Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library, Hien Luu represents a material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool. This resource is a combination of several types including: Instance, Electronic.

The Resource Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library, Hien Luu

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Label: Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library, Hien Luu

Title remainder: with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library

Statement of responsibility: Hien Luu

Instantiates

Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library

Publication

Berkeley, Apress, 2018

Distribution

New York, NY, Distributed to the book trade worldwide by Springer,

Copyright

Note: Includes index

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Carrier category: online resource

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Carrier MARC source: rdacarrier

Color: multicolored

Content category: text

Content type code

Content type MARC source: rdacontent

Contents

Intro; Table of Contents; About the Author; About the Technical Reviewer; Chapter 1: Introduction to  Apache Spark; Overview; History; Spark Core Concepts and Architecture; Spark Clusters and the Resource Management System; Spark Application; Spark Driver and Executor; Spark Unified Stack; Spark Core; Spark SQL; Spark Structured Streaming and Streaming; Spark MLlib; Spark Graphx; SparkR; Apache Spark Applications; Example Spark Application; Summary; Chapter 2: Working with Apache Spark; Downloading and Installing Spark; Downloading Spark; Installing Spark; Spark Scala Shell
Spark Python ShellHaving Fun with the Spark Scala Shell; Useful Spark Scala Shell Commands and Tips; Basic Interactions with Scala and Spark; Basic Interactions with Scala; Spark UI and Basic Interactions with Spark; Spark UI; Basic Interactions with Spark; Introduction to Databricks; Creating a Cluster; Creating a Folder; Creating a Notebook; Setting Up the Spark Source Code; Summary; Chapter 3: Resilient Distributed Datasets; Introduction to RDDs; Immutable; Fault Tolerant; Parallel Data Structures; In-Memory Computing; Data Partitioning and Placement; Rich Set of Operations; RDD Operations
Creating RDDsTransformations; Transformation Examples; map(func); flatMap(func); filter(func); mapPartitions(func)/mapPartitionsWithIndex(index, func); union(otherRDD); intersection(otherRDD); substract(otherRDD); distinct(); sample(withReplacement, fraction, seed); Actions; Action Examples; collect(); count(); first(); take(n); reduce(func); takeSample(withReplacement, n, [seed]); takeOrdered(n, [ordering]); top(n, [ordering]); saveAsTextFile(path); Working with Key/Value Pair RDD; Creating Key/Value Pair RDD; Key/Value Pair RDD Transformations; groupByKey([numTasks])
ReduceByKey(func, [numTasks])sortByKey([ascending],[numTasks]); join(otherRDD); Key/Value Pair RDD Actions; countByKey(); collectAsMap(); lookup(key); Understand Data Shuffling; Having Fun with RDD Persistence; Summary; Chapter 4: Spark SQL (Foundations); Introduction to DataFrames; Creating DataFrames; Creating DataFrames from RDDs; Creating DataFrames from a Range of Numbers; Creating DataFrames from Data Sources; Creating DataFrames by Reading Text Files; Creating DataFrames by Reading CSV Files; Creating DataFrames by Reading JSON Files; Creating DataFrames by Reading Parquet Files
Creating DataFrames by Reading ORC FilesCreating DataFrames from JDBC; Working with Structured Operations; Working with Columns; Working with Structured Transformations; select(columns); selectExpr(expressions); filler(condition), where(condition); distinct, dropDuplicates; sort(columns), orderBy(columns); limit(n); union(otherDataFrame); withColumn(colName, column); withColumnRenamed(existingColName, newColName); drop(columnName1, columnName2); sample(fraction), sample(fraction, seed), sample(fraction, seed, withReplacement); randomSplit(weights); Working with Missing or Bad Data

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Isbn: 9781484235782

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Specific material designation: remote

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on1049171799
(OCoLC)1049171799

Context

Context of Beginning Apache Spark 2 : with resilient distributed datasets, Spark SQL, structured streaming and Spark Machine Learning library, Hien Luu

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