Apache Spark Course

Apache Spark is an open-source cluster computing framework.In contrast to Hadoop’s two-stage disk-based MapReduce paradigm, Spark’s in-memory primitives provide performance up to 100 times faster for certain applications.

Apache Spark

Apache Spark Training Course Introduction

Apache Spark is an open-source cluster computing framework.

In contrast to Hadoop’s two-stage disk-based MapReduce paradigm, Spark’s in-memory primitives provide performance up to 100 times faster for certain applications.

By allowing user programs to load data into a cluster’s memory and query it repeatedly, Spark is well suited to machine learning algorithms. Spark requires a cluster manager and a distributed storage system.

For cluster management, Spark supports standalone (native Spark cluster), Hadoop YARN, or Apache Mesos.

For distributed storage, Spark can interface a wide variety, including Hadoop Distributed File System (HDFS) , Cassandra, OpenStack Swift, and Amazon S3.

Spark also supports a pseudo-distributed local mode, usually used only for development or testing purposes, where distributed storage is not required and the local file system can be used instead.

Spark Training Curriculum

Introduction To Big Data and Spark

Learn how to apply data science techniques using parallel programming during Spark training, to explore big (and small) data.

• Introduction to Big Data
• Challenges with Big Data
• Batch Vs. Real Time Big Data Analytics
• Batch Analytics – Hadoop Ecosystem Overview
• Real Time Analytics Options
• Streaming Data – Storm
• In Memory Data – Spark
• What is Spark?
• Modes of Spark
• Spark Installation Demo
• Overview of Spark on a cluster
• Spark Standalone Cluster

Spark Baby Steps

Learn how to invoke spark shell, build spark project with sbt , distributed persistence and much more…in this module of Spark training.

• Invoking Spark Shell
• Creating the Spark Context
• Loading a File in Shell
• Performing Some Basic Operations on Files in Spark Shell
• Building a Spark Project with sbt
• Running Spark Project with sbt
• Caching Overview
• Distributed Persistence
• Spark Streaming Overview
• Example: Streaming Word Count

Playing With RDDs In Spark

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel.

• RDDs
• Spark Transformations in RDD
• Actions in RDD
• Loading Data in RDD
• Saving Data through RDD
• Spark Key-Value Pair RDD
• Map Reduce and Pair RDD Operations in Spark
• Scala and Hadoop Integration Hands on

Shark – When Spark Meets Hive

Shark is a component of Spark, an open source, distributed and fault-tolerant, in-memory analytics system, that can be installed on the same cluster as Hadoop. This module of spark training, will give insights about Shark.

• Why Shark?
• Installing Shark
• Running Shark
• Loading of Data
• Hive Queries through Spark
• Testing Tips in Scala
• Performance Tuning Tips in Spark
• Shared Variables: Broadcast Variables

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What is Apache Spark?

Spark is a fast, easy-to-use and flexible data processing framework. It has an advanced execution engine supporting cyclic data  flow and in-memory computing. Spark can run on Hadoop, standalone or in the cloud and is capable of accessing diverse data sources including HDFS, HBase, Cassandra and others. Learn more in this Apache Spark Tutorial .

.Explain key features of Spark.

• Allows Integration with Hadoop and files included in HDFS.
• Spark has an interactive language shell as it has an independent Scala (the language in which Spark is written) interpreter
• Spark consists of RDD’s (Resilient Distributed Datasets), which can be cached across computing nodes in a cluster.
• Spark supports multiple analytic tools that are used for interactive query analysis , real-time analysis and graph processing

Define RDD.

RDD is the acronym for Resilient Distribution Datasets – a fault-tolerant collection of operational elements that run parallel. The partitioned data in RDD is immutable and distributed. There are primarily two types of RDD:
• Parallelized Collections : The existing RDD’s running parallel with one another
• Hadoop datasets: perform function on each file record in HDFS or other storage system

What does a Spark Engine do?

Spark Engine is responsible for scheduling, distributing and monitoring the data application across the cluster.

What operations RDD support?

• Transformations
• Actions

Key Features


Overview of Course and Learning analytic’s


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Course Curriculam

Apache Spark Course Modules

Duration: 45 Days

Support: 24×7

Video: Yes

Apache Spark Certified Professional