Apache Spark and Scala Certification Training Course by Edureka: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

Course overview

The Apache Spark and Scala Certification Training course by Edureka will help you master the fundamentals of Hadoop and Big Data. You will learn in-depth about Apache Spark and its ecosystem; including Spark SQL for process structuring, Spark Streaming, Spark RDD, Spark MLib, and other Spark APIs. You will also develop an understanding of Scala, Flume, Sqoop, GraphX, and Kafka, among others.

An instructor-led training course, Apache Spark and Scala Certification Training will help you to understand the capabilities of data ingestion using Sqoop and Flume in HDFS. Moreover, the course will also cover the core principles of Apache and Scala; such as Flume data capture, Sqoop data loading, and Kafka messaging framework. Over the course duration, you will also get to work on many Big Data and analytics ventures.

Furthermore, the Apache Spark and Scala Certification Training course has been curated by industry experts and aims to provide you with the expertise and skills to become a qualified Apache Spark and Scala Developer.

Course and certificate fees

Fees information

₹ 18,695 ₹21,995

The Apache Spark and Scala Certification Training course fee has two components: program fee and GST. You can pay the course fee in either lump sum or EMIs. A 10% discount is also available on the course fee for a limited time.

Apache Spark and Scala Certification Training Fee Details

Training Option	Fee in INR
Instructor-led Training programme	Rs. 21,995 (Plus GST)
GST	18% (Rs. 3,959.10)
Amount Payable	Rs. 25,954.10

*No Cost EMI is also available

*Discounts are variable*

certificate availability

Yes

certificate providing authority

Edureka

Who it is for

Data scientist Database architect

The professionals who can benefit from the Edureka Apache Spark and Scala Certification course comprise:

Engineers
Software Architects
Developers
Business Intelligence Professionals
Data Warehouse Architects
Mainframe Professionals
Experienced IT Professionals
Data Developers
Analytics Professionals
Data Architects
Testing Professionals
Big Data Enthusiasts
Data Scientists

What you will learn

Knowledge of big data Knowledge of apache spark

After completing the Edureka Apache Spark and Scala Certification Training programme, you can become proficient in the following skills:

Understanding Hadoop, Hadoop 2.x architecture, and Big Data while working with the concepts of YARN.
Ability to ingest data using Flume and Sqoop in HDFS, and analysing large HDFS datasets.
Master the use and implementation of the different tools that fall in Spark and its ecosystem.
Learn to leverage real-time data feeds through a system such as Kafka.
Become proficient in the use of Spark RDD techniques to write Spark Applications and master building Apache Spark apps by writing Scala programs.
Learn to develop solutions for real-time Big Data challenges with hands-on experience of the Hadoop framework.
Gain knowledge of the implementation of Spark apps over Hadoop YARN and Spark operations over Spark Shell.
Learn to implement various Machine Learning algorithms in MLib API (Spark) and Spark Streaming.
Learn to leverage Kafka for producing and consuming messages.
Ability to implement the best practises based on the current industry criteria.
Ability to work on real-world Big Data and other Apache Spark and Scala Ecosystem projects.

The syllabus

Introduction to Big Data Hadoop and Spark

Topics

What is Big Data?
Big Data Customer Scenarios
Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
How Hadoop Solves the Big Data Problem?
What is Hadoop?
Hadoop’s Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Rack Awareness and Block Replication
YARN and its Advantage
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Hadoop Terminal Commands
Big Data Analytics with Batch & Real-time Processing
Why Spark is needed?
What is Spark?
How Spark differs from other frameworks?
Spark at Yahoo!

Hands-on

Hadoop terminal commands

Introduction to Scala for Apache Spark

Topics

What is Scala?
Why Scala for Spark?
Scala in other Frameworks
Introduction to Scala REPL
Basic Scala Operations
Variable Types in Scala
Control Structures in Scala
Foreach loop, Functions and Procedures
Collections in Scala- Array
ArrayBuffer, Map, Tuples, Lists, and more

Hands-on

Control structures in Scala
Working with various looping statements
Implementing collections in Scala

Functional Programming and OOPs Concepts in Scala

Topics

Functional Programming
Higher Order Functions
Anonymous Functions
Class in Scala
Getters and Setters
Custom Getters and Setters
Properties with only Getters
Auxiliary Constructor and Primary Constructor
Singletons
Extending a Class
Overriding Methods
Traits as Interfaces and Layered Traits

Hands-on

Creating objects and classes
Working with higher order functions
Creating constructors in Scala

Deep Dive into Apache Spark Framework

Topics

Spark’s Place in Hadoop Ecosystem
Spark Components & its Architecture
Spark Deployment Modes
Introduction to Spark Shell
Writing your first Spark Job Using SBT
Submitting Spark Job
Spark Web UI
Data Ingestion using Sqoop

Hands-on

Building and Running Spark Application
Spark Application Web UI
Configuring Spark Properties
Data ingestion using Sqoop

Playing with Spark RDDs

Topics

Challenges in Existing Computing Methods
Probable Solution & How RDD Solves the Problem
What is RDD, It’s Operations, Transformations & Actions
Data Loading and Saving Through RDDs
Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
WordCount Program Using RDD Concepts
RDD Partitioning & How It Helps Achieve Parallelization
Passing Functions to Spark

Hands-on

Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
WordCount through RDDs

DataFrames and Spark SQL

Topics

Need for Spark SQL
What is Spark SQL?
Spark SQL Architecture
SQL Context in Spark SQL
User Defined Functions
Data Frames & Datasets
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources
Spark – Hive Integration

Hands-on

Spark SQL – Creating Data Frames
Loading and Transforming Data through Different Sources
Stock Market Analysis
Spark-Hive Integration

Machine Learning using Spark MLlib

Topics

Why Machine Learning?
What is Machine Learning?
Where Machine Learning is Used?
Face Detection: USE CASE
Different Types of Machine Learning Techniques
Introduction to MLlib
Features of MLlib and MLlib Tools
Various ML algorithms supported by MLlib

Hands-on

Face detection use case

Deep Dive into Spark MLlib

Topics

Supervised Learning - Linear Regression, Logistic Regression, Decision Tree, Random Forest
Unsupervised Learning - K-Means Clustering & How It Works with MLlib
Analysis on US Election Data using MLlib (K-Means)

Hands-on

Machine Learning MLlib
K- Means Clustering
Linear Regression
Logistic Regression
Decision Tree
Random Forest

Understanding Apache Kafka and Apache Flume

Topics

Need for Kafka
What is Kafka?
Core Concepts of Kafka
Kafka Architecture
Where is Kafka Used?
Understanding the Components of Kafka Cluster
Configuring Kafka Cluster
Kafka Producer and Consumer Java API
Need of Apache Flume
What is Apache Flume?
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration
Integrating Apache Flume and Apache Kafka

Hands-on

Configuring Single Node Single Broker Cluster
Configuring Single Node Multi Broker Cluster
Producing and consuming messages
Flume Commands
Setting up Flume Agent
Streaming Twitter Data into HDFS

Apache Spark Streaming - Processing Multiple Batches

Topics

Drawbacks in Existing Computing Methods
Why Streaming is Necessary?
What is Spark Streaming?
Spark Streaming Features
Spark Streaming Workflow
How Uber Uses Streaming Data
Streaming Context & DStreams
Transformations on DStreams
Describe Windowed Operators and Why it is Useful
Important Windowed Operators
Slice, Window and ReduceByWindow Operators
Stateful Operators

Hands-on

Creating a DStream
Transformation on DStreans
Creating streaming context

Apache Spark Streaming - Data Sources

Topics

Apache Spark Streaming: Data Sources
Streaming Data Source Overview
Apache Flume and Apache Kafka Data Sources
Example: Using a Kafka Direct Data Source
Perform Twitter Sentimental Analysis Using Spark Streaming

Hands-on

Different Streaming Data Sources
Integrating Spark with Kafka and Flume
Twitter Sentimental Analysis

Admission details

Follow the steps mentioned below to enroll in the Edureka Apache Spark and Scala Certification Training programme:

Step 1 – Visit the official website of Edureka: https://www.edureka.co/

Step 2 – Search the "Big Data Hadoop Certification Training" course in the search bar on the website.

Step 3 – Click on the “Enroll now” button present on the top of the page.

Step 4 – Once you click on the "Enroll Now" button, you will be redirected to another webpage to fill the registration form.

Step 5 – Next, you need to fill out the online application. Enter your contact information, such as email and phone number.

Step 6 – Once you fill the details, you need to pick a method of payment and pay the applicable fee. Also, download the transaction receipt for future reference.

Filling the form

To enrol in the Apache Spark and Scala Qualification Training online course, you need to fill an application form on the Edureka website. You need to provide your contact details as requested in the application form. After you have filled out the details in the form, you need to pick a payment mode and make the payment to validate your submission.

Evaluation process

No examination is held for certification. Instead, candidates need to complete a certification project to implement the concepts acquired in the training course. They can mail the project to the support team of Edureka, within two weeks from the course completion date. A group of experts will evaluate the project based on performance to provide a grade and award the certificate for the course.

How it helps

With the Apache Spark and Scala Certification Training course, you will develop core skills essential to become an Apache Spark and Scala Developer. You will take on real-world Big Data and analytics projects with guidance from Spark professionals.

Besides, upon the successful completion of the Apache Spark and Scala Certification Training course, you will receive a certificate of achievement. You will gain expertise in concepts and techniques, ranging from the implementation of business logic to processing structured data via SQL queries.

You can easily find lucrative roles in companies such as Amazon, IBM, and Microsoft, Facebook, Netflix, and LinkedIn. Once you finish the Edureka Apache Spark and Scala Certification Training course, plenty of career opportunities will be available for you.

FAQs

How long does it take to complete the Apache Spark and Scala Certification Training course by Edureka?

The Apache Spark and Scala Certification Training course is of 6 weeks and weekend batches are available for the training programme.

Will I get a certificate after completing the Edureka training course?

Yes, Edureka offers you the Apache Spark and Scala Developer certification upon successful completion of the course.

Who are the instructors for the Apache Spark and Scala Certification Training programme?

The instructors at Edureka are experts with over ten years of experience, who have been selected via a comprehensive screening process. Moreover, all the instructors undergo a 3-month preparation course after being shortlisted.

Does Apache Spark and Scala Certification Training by Edureka offer a self-paced learning model?

No, the Apache Spark and Scala Certification Training course by Edureka is a live programme led by an instructor. If you miss any class, you can either take the session in another batch or view the session recording.