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Working with Apache Spark (Aug 2023)

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Working with Apache Spark (Aug 2023), available at $19.99, has an average rating of 4.3, with 28 lectures, 8 quizzes, based on 5 reviews, and has 75 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists / Data Engineers or Big Data Developers or Big Data Engineers or Big Data Architects or Any technical personnel who are interested in learning and Exploring the Features of Apache Spark It is particularly useful for Data Scientists / Data Engineers or Big Data Developers or Big Data Engineers or Big Data Architects or Any technical personnel who are interested in learning and Exploring the Features of Apache Spark.

Enroll now: Working with Apache Spark (Aug 2023)

Summary

Title: Working with Apache Spark (Aug 2023)

Price: $19.99

Average Rating: 4.3

Number of Lectures: 28

Number of Quizzes: 8

Number of Published Lectures: 28

Number of Published Quizzes: 8

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: ₹3,299

Quality Status: approved

Status: Live

What You Will Learn

  • 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

Who Should Attend

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

Target Audiences

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

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 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 includes 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 is 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.

The course teaches you everything you need to know about Apache Spark.

This course gives details about Working with Apache Spark with an emphasis on its activity lessons and hands on experience.

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Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Introduction to Spark

Lecture 1: Lesson 01: Introduction to Spark

Lecture 2: Practice 1-1: Installing and Configuring Standalone Spark Computing Environment

Lecture 3: Practice 1-2: Create & Run a Project on Standalone Spark Programming Environment

Chapter 3: Spark Runtime: Context, Executor and Stage

Lecture 1: Lesson 02: Spark Runtime: Context, Executor and Stage

Lecture 2: Practice 2-1: Starting Spark on Cloudera Hadoop Ecosystem

Lecture 3: Practice 2-2: Exploring the Spark Context in the Cloudera Ecosystem

Chapter 4: Working with Spark RDD

Lecture 1: Lesson 03: Working with Spark RDD

Lecture 2: Practice 3-1: Working with RDD Transformations and Actions

Lecture 3: Practice 3-2: Working with Paired RDDs

Lecture 4: Practice 3-3: Using Cache and Persist Methods in Spark

Chapter 5: Working with Spark SQL

Lecture 1: Lesson 04: Working with Spark SQL

Lecture 2: Practice 4-1: DataFrames in Spark SQL

Lecture 3: Practice 4-2: DataSets in Spark SQL

Lecture 4: Practice 4-3: Using Window Function in Spark SQL

Lecture 5: Practice 4-4: Creating DataFrame and Dataset in Standalone Spark Environment

Chapter 6: Working with Spark Streaming

Lecture 1: Lesson 05: Working with Spark Streaming

Lecture 2: Practice 5-1: Installing and Running Apache Kafka in a Standalone Environment

Lecture 3: Practice 5-2: Spark Streaming with Apache Kafka

Chapter 7: Using Sparklyr

Lecture 1: Lesson 06: Using Sparklyr

Lecture 2: Practice 6-1: Configuring R Programming Environment

Lecture 3: Practice 6-2: Working with Spark DataFrames in R

Chapter 8: Advanced Features with Spark

Lecture 1: Lesson 07: Advanced Features with Spark

Lecture 2: Practice 7-1: Performing various Spark Operations using Pyspark

Lecture 3: Practice 7-2: Working with GraphX

Lecture 4: Practice 7-3: Implementing Linear Regression using MLlib

Chapter 9: Executing and Scheduling the Spark job

Lecture 1: Lesson 08: Executing and Scheduling the Spark job

Lecture 2: Practice 8-1: Running Spark Python Application

Instructors

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  • 5 stars: 2 votes

Frequently Asked Questions


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