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Working with Apache Spark – web courses

Working with Apache Spark - web courses
Working with Apache Spark - web courses

Working with Apache Spark – web courses

A Complete Primer for Apache Spark

What you’ll learn

Working with Apache Spark – web courses

  • Apache Spark and its features
  • Installing and Configuring Spark Programming Environment
  • Spark Programming using Scala
  • Creating and Working with Spark Context, Spark RDD, DataFrames, DataSets
  • Transformations and Actions using DataFrames
  • Spark SQL, Spark Streaming with Kafka, GraphX, Spark Mllib, PySpark, and Sparklyr
  • Scheduling Spark Jobs

Requirements

  • Working Knowledge on Cloudera Hadoop Stack
  • Basic Programming Knowledge
  • Basic Linux Commands

Description

In this course, you will learn in detail about Apache Spark and its Features. This is course deep dives into Features of Apache Spark, RDDs, Transformation, Actions, Lazy Execution, Data Frames, DataSets, Spark SQL, Spark Streaming, PySpark, Sparklyr, and Spark Jobs.

You will explore creating Spark RDD and performing various transformation operations on RDDs along with actions. This Course also illustrates the difference between RDD, DataFrame, and DataSet with examples. You will also explore the features of Spark SQL and execute database queries using various contexts.

In this course, you will also explore Spark Streaming along with Kafka. The Spark Streaming examples include producing and consuming messages on a Kafka Topic. Spark program is basically coded using Scala in this course, but PySpark is also discussed, programming examples using PySpark are also included.

Usage of Sparklyr package in R Programming is included in the Course. Finally, the course includes how to schedule and execute Spark Jobs.

Who this course is for:

  • Data Scientists / Data Engineers
  • Big Data Developers
  • The Big Data Engineers
  • Big Data Architects
  • Any technical personnel who are interested in learning and Exploring the Features of Apache Spark
  • Last updated 9/2020

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