What you'll 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

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 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.

Who this course is for:
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