Apache Spark Training

Live Online & Classroom Enterprise Training

With our Apache Spark training, master the skills you need to develop complete, unified big data applications combining batch, streaming, and interactive analytics on your organization’s big data.

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What is Apache Spark Training about?

Our Apache Spark certification training prepares to you master Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. You also gain the skills required to work with large datasets stored in a distributed file system, and execute Spark applications on a Hadoop cluster at your organization.

By the end of our Spark training course, you will gain a deep understanding of Spark architecture and what makes it better and faster than MapReduce. With easy to follow, step-by-step instructions, trainees learn how to create and operate on data frames from all their organization's data sources.

With Cloudlabs, the virtual lab environment, gain hands-on experience querying tables and views in Spark SQL. In our Apache Spark course, you will also learn how to write sophisticated parallel applications to execute faster decisions that is applicable to a wide variety of use cases.

What are the objectives of Apache Spark Training ?

At the end of this Apache Spark certification training, you will be able to:

Understand the architecture of Spark and explain its business use cases
Distribute, store, and process data using RDDs in a Hadoop cluster
Use Spark SQL for querying DBs
Write, configure, and deploy Spark applications on a cluster
Use the Spark shell for interactive data analysis
Process and query structured data using Spark SQL

Who is Apache Spark Training for?

Developers working on Spark business intelligence implementation
Teams getting started or working on Spark projects

What are the prerequisites for Apache Spark Training?

Knowledge of Apache Hadoop ecosystem, SQL, Linux CLI and Scala is required.

Available Training Modes

Live Online Training

12 Hours

Classroom Training

2 Days

Course Outline Expand All

Expand All

Apache Spark Basics

What is Apache Spark?

Starting the Spark Shell

Using the Spark Shell

Getting Started with Datasets and DataFrames

DataFrame Operations

Working with DataFrames and Schemas

Working with DataFrames and Schemas

Creating DataFrames from Data Sources

Saving DataFrames to Data Sources

DataFrame Schemas

Eager and Lazy Execution

Analyzing Data with DataFrame Queries

Querying DataFrames Using

Column Expressions

Grouping and Aggregation Queries

Joining DataFrames

RDD Overview

RDD Overview

RDD Data Sources

Creating and Saving RDDs

RDD Operations

Transforming Data with RDDs

Writing and Passing

Transformation Functions

Transformation Execution

Converting Between RDDs and DataFrames

Querying Tables and Views with Apache Spark SQL

Querying Tables in Spark Using SQL

Querying Files and Views

The Catalog API

Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark

Writing, Configuring, and Running

Apache Spark Applications

Writing a Spark Application

Building and Running an Application

Application Deployment Mode

The Spark Application Web UI

Configuring Application Properties

Distributed Processing

Review: Apache Spark on a Cluster

RDD Partitions

Example: Partitioning in Queries

Stages and Tasks

Job Execution Planning

Example: Catalyst Execution Plan

Example: RDD Execution Plan

Common Patterns in Apache Spark

Data Processing

Common Apache Spark Use Cases

Iterative Algorithms in Apache Spark

Machine Learning

Example: k-means

Who is the instructor for this training?

The trainer for this Apache Spark online training has extensive experience in deploying and managing Hadoop ecosystems as well as years of Apache Spark training experience.

Reviews

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Sony

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Nikhil Kohli

Stryker

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Heena Rajan

Mindtree