Connecting to data sources through SQL APIs # _*_ coding: utf-8 _*_ from __future__ import print_function from pyspark.sql import SparkSession if __name__ == "__main__": # Create a SparkSession session. sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate(...
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package org.apache.spark.internal.config import java.util.{Map => JMap} import java.util.regex.Pattern import scala.collection.mutable.HashMap import scala.util.matching.Regex private object ConfigReader { private val REF_RE = "\\$\\{(?:(\\w+?):)?(\\S+?)\\}".r } def substitute(...
asInstanceOf[String] val preppedStatement = convert(conf, sparkHome) submit(preppedStatement, sparkHome) } private def convert(conf: SparkJobConf, sparkHome: String): Seq[String] = { Seq(s"$sparkHome/bin/spark-submit") ++ Seq("--class", conf.className) ++ convertSparkArgs(conf.sparkArgs...
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By reading the data using a Spark Session, it is possible to perform basic exploratory analysis computations without actually trying to load the complete data set into memory. This type of approach can be useful when we want to be able to get a first impression of the data and search for ...
to make the most out of new opportunities. What you will learn * Understand the important concepts in machine learning and data science * Use Python to explore the world of data mining and analytics * Scale up model training using varied data complexities with Apache Spark * Delve deep into ...