Apache Spark and Scala Certification Training by Simplilearn: 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

Course overview

The Apache Spark and Scala online training is designed to get students comfortable with the industry-standard platforms that are Apache Spark and Scala. This Apache Spark and Scala Certification Training course by Edureka takes the students through a variety of Spark concepts such as Spark Streaming, machine learning programming, GraphX programming, Spark SQL, and Shell Scripting in Spark.

Expand your skill-set of the Big Data Hadoop frameworks with the Apache Spark and Scala certification training course, which is developed to help candidates attain expertise in crucial Apache Spark skills, providing the candidates with a competitive edge for a career in Big Data.

Moreover, the Apache Spark and Scala training course also help the students in building proficiency in the Scala programming language and Scala topics like data types, basic literals, operators, functions, objects, classes, lists, maps, and data structures.

In addition, the Apache Spark and Scala course will make the candidates ready for any Big Data-based industry role that requires proficiency in Spark and Scala. Apache Spark and Scala Certification Training course by Edureka also helps the students prepare for the CCA175 Spark and Hadoop Developer exam to become a Spark Developer.

Course and certificate fees

certificate availability

Yes

certificate providing authority

Simplilearn

Who it is for

Data scientist

The Apache Spark and Scala Certification is a perfect fit for professionals such as –

IT developers and testers: Developers interested in Scala programming
Professionals working in the Analytics domain who wish to upskill
Professionals currently working as Data scientists
Students currently pursuing their UG/PG in a related field

Eligibility criteria

Skills

There are no prerequisites to join the course. However, candidates who wish to join Apache Spark and Scala should have a fundamental knowledge of any programming language, database, SQL, and query language for databases. In addition, they should have a working knowledge of Linux- or Unix-based systems.

What you will learn

Knowledge of apache spark

Candidates will also gain proficiency in the Scala programming language. In the Apache Spark and Scala course, candidates will learn the following skillset:

Gain proficiency in the Scala programming language
Learn how to install Spark on their system of choice
Tune the settings and preferences in Spark to optimize workflow
know the various streaming features of Spark like Integration, Combination, Load balancing, resource usage, and recovery from stragglers and failures.
Gain knowledge about the machine learning package recently added to the Spark library
Use Spark ML is to provide high-level APIs and create machine learning pipelines
Attain a working knowledge of Spark SQL
Understand the Spark SQL architecture, handle various data formats, implement Data Frame operations, and process DataFrames.
Learn about the resilient Distributed Dataset (RDDs) and its applications
Learn how to create RDDs, RDD operations, and RDD extensions

The syllabus

Apache Spark & Scala

Module 1 - Course Overview

Introduction
Course Objectives
Course Overview
Target Audience
Course Prerequisites
Value to the Professionals
Value to the Professionals (contd.)
Value to the Professionals (contd.)
Lessons Covered
Conclusion

Module 2 - Introduction to Spark

Introduction
Objectives
Evolution of Distributed Systems
Need of New Generation Distributed Systems
Limitations of MapReduce in Hadoop
Limitations of MapReduce in Hadoop (contd.)
Batch vs. Real-Time Processing
PairRDD Methods – Others
Application of In-Memory Processing
Introduction to Apache Spark
Components of a Spark Project
History of Spark
Language Flexibility in Spark
Spark Execution Architecture
Automatic Parallelization of Complex Flows
Automatic Parallelization of Complex Flows – Important Points
APIs That Match User Goals
Apache Spark – A Unified Platform of Big Data Apps
More Benefits of Apache Spark
Running Spark in Different Modes
Installing Spark as a Standalone Cluster – Configurations
Installing Spark as a Standalone Cluster – Configurations
Demo – Install Apache Spark
Demo – Install Apache Spark
Overview of Spark on a Cluster
Tasks of Spark on a Cluster
Companies Using Spark – Use Cases
Hadoop Ecosystem vs. Apache Spark
Hadoop Ecosystem vs. Apache Spark (contd.)
Quiz
Summary
Summary (contd.)
Conclusion

Module 3 - Introduction to Programming in Scala

Introduction
Objectives
Introduction to Scala
Features of Scala
Basic Data Types
Basic Literals
Basic Literals (contd.)
Basic Literals (contd.)
Introduction to Operators
Types of Operators
Use Basic Literals and the Arithmetic Operator
Demo – Use Basic Literals and the Arithmetic Operator
Use the Logical Operator
Demo – Use the Logical Operator
Introduction to Type Inference
Type Inference for Recursive Methods
Type Inference for Polymorphic Methods and Generic Classes
Unreliability on Type Inference Mechanism
Mutable Collection vs. Immutable Collection
Functions
Anonymous Functions
Objects
Classes
Use Type Inference, Functions, Anonymous Function, and Class
Demo – Use Type Inference, Functions, Anonymous Function and Class
Traits as Interfaces
Traits – Example
Collections
Types of Collections
Types of Collections (contd.)
Lists
Perform Operations on Lists
Demo – Use Data Structures
Maps
Maps – Operations
Pattern Matching
Implicits
Implicits (contd.)
Streams
Use Data Structures
Demo – Perform Operations on Lists
Quiz
Summary
Summary (contd.)
Conclusion

Module 4 - Using RDD for Creating Applications in Spark

Introduction
Objectives
RDDs API
Features of RDDs
Creating RDDs
Creating RDDs – Referencing an External Dataset
Referencing an External Dataset – Text Files
Referencing an External Dataset – Text Files (contd.)
Referencing an External Dataset – Sequence Files
Referencing an External Dataset – Other Hadoop Input Formats
Creating RDDs – Important Points
RDD Operations
RDD Operations – Transformations
Features of RDD Persistence
Storage Levels of RDD Persistence
Choosing the Correct RDD Persistence Storage Level
Invoking the Spark Shell
Importing Spark Classes
Creating the SparkContext
Loading a File in Shell
Performing Some Basic Operations on Files in Spark Shell RDDs
Packaging a Spark Project with SBT
Running a Spark Project with SBT
Demo – Build a Scala Project
Build a Scala Project
Demo – Build a Spark Java Project
Build a Spark Java Project
Shared Variables – Broadcast
Shared Variables – Accumulators
Writing a Scala Application
Demo – Run a Scala Application
Run a Scala Application
Demo – Write a Scala Application Reading the Hadoop Data
Write a Scala Application Reading the Hadoop Data
Demo – Run a Scala Application Reading the Hadoop Data
Run a Scala Application Reading the Hadoop Data
Scala RDD Extensions
DoubleRDD Methods
PairRDD Methods – Join
PairRDD Methods – Others
Java PairRDD Methods
Java PairRDD Methods (contd.)
General RDD Methods
General RDD Methods (contd.)
Java RDD Methods
Java RDD Methods (contd.)
Common Java RDD Methods
Spark Java Function Classes
Method for Combining JavaPairRDD Functions
Transformations in RDD
Other Methods
Actions in RDD
Key-Value Pair RDD in Scala
Key-Value Pair RDD in Java
Using MapReduce and Pair RDD Operations
Reading Text File from HDFS
Reading Sequence File from HDFS
Writing Text Data to HDFS
Writing Sequence File to HDFS
Using GroupBy
Using GroupBy (contd.)
Demo – Run a Scala Application Performing GroupBy Operation
Run a Scala Application Performing GroupBy Operation
Demo – Run a Scala Application Using the Scala Shell
Run a Scala Application Using the Scala Shell
Demo – Write and Run a Java Application
Write and Run a Java Application
Quiz
Summary
Summary (contd.)
Conclusion

Module 5 - Running SQL queries Using SparkSQL

Introduction
Objectives
Importance of Spark SQL
Benefits of Spark SQL
DataFrames
SQLContext
SQLContext (contd.)
Creating a DataFrame
Using DataFrame Operations
Using DataFrame Operations (contd.)
Demo – Run Spark SQL with a DataFrame
Run Spark SQL with a DataFrame
Interoperating with RDDs
Using the Reflection-Based Approach
Using the Reflection-Based Approach (contd.)
Using the Programmatic Approach
Using the Programmatic Approach (contd.)
Demo – Run Spark SQL Programmatically
Run Spark SQL Programmatically
Data Sources
Save Modes
Saving to Persistent Tables
Parquet Files
Partition Discovery
Schema Merging
JSON Data
Hive Table
DML Operation – Hive Queries
Demo – Run Hive Queries Using Spark SQL
Run Hive Queries Using Spark SQL
JDBC to Other Databases
Supported Hive Features
Supported Hive Features (contd.)
Supported Hive Data Types
Case Classes
Case Classes (contd.)
Quiz
Summary
Summary (contd.)
Conclusion

Module 6 - Spark Streaming

Introduction
Objectives
Introduction to Spark Streaming
Working of Spark Streaming
Features of Spark Streaming
Streaming Word Count
Micro Batch
DStreams
DStreams (contd.)
Input DStreams and Receivers
Input DStreams and Receivers (contd.)
Basic Sources
Advanced Sources
Advanced Sources – Twitter
Transformations on DStreams
Transformations on DStreams (contd.)
Output Operations on DStreams
Design Patterns for Using ForeachRDD
DataFrame and SQL Operations
DataFrame and SQL Operations (contd.)
Checkpointing
Enabling Checkpointing
Socket Stream
File Stream
Stateful Operations
Window Operations
Types of Window Operations
Types of Window Operations (contd.)
Join Operations – Stream-Dataset Joins
Join Operations – Stream-Stream Joins
Monitoring Spark Streaming Application
Performance Tuning – High Level
Performance Tuning – Detail Level
Demo – Capture and Process the Netcat Data
Capture and Process the Netcat Data
Demo – Capture and Process the Flume Data
Capture and Process the Flume Data
Demo – Capture the Twitter Data
Capture the Twitter Data
Quiz
Summary
Summary (contd.)
Conclusion

Module 7 - Spark ML Programming

Introduction
Objectives
Introduction to Machine Learning
Common Terminologies in Machine Learning
Applications of Machine Learning
Machine Learning in Spark
Spark ML API
DataFrames
Transformers and Estimators
Pipeline
Working of a Pipeline
Working of a Pipeline (contd.)
DAG Pipelines
Runtime Checking
Parameter Passing
General Machine Learning Pipeline – Example
General Machine Learning Pipeline – Example (contd.)
Model Selection via Cross-Validation
Supported Types, Algorithms, and Utilities
Data Types
Feature Extraction and Basic Statistics
Clustering
K-Means
K-Means (contd.)
Demo – Perform Clustering Using K-Means
Perform Clustering Using K-Means
Gaussian Mixture
Power Iteration Clustering (PIC)
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) (contd.)
Collaborative Filtering
Classification
Classification (contd.)
Regression
Example of Regression
Demo – Perform Classification Using Linear Regression
Perform Classification Using Linear Regression
Demo – Run Linear Regression
Run Linear Regression
Demo – Perform Recommendation Using Collaborative Filtering
Perform Recommendation Using Collaborative Filtering
Demo – Run Recommendation System
Run Recommendation System
Quiz
Summary
Summary (contd.)
Conclusion

Module 9 - Spark GraphX Programming

Introduction
Objectives
Introduction to Graph-Parallel System
Limitations of Graph-Parallel System
Introduction to GraphX
Introduction to GraphX (contd.)
Importing GraphX
The Property Graph
The Property Graph (contd.)
Features of the Property Graph
Creating a Graph
Demo – Create a Graph Using GraphX
Create a Graph Using GraphX
Triplet View
Graph Operators
List of Operators
List of Operators (contd.)
Property Operators
Structural Operators
Subgraphs
Join Operators
Demo – Perform Graph Operations Using GraphX
Perform Graph Operations Using GraphX
Demo – Perform Subgraph Operations
Perform Subgraph Operations
Neighborhood Aggregation
mapReduceTriplets
Demo – Perform MapReduce Operations
Perform MapReduce Operations
Counting Degree of Vertex
Collecting Neighbors
Caching and Uncaching
Graph Builders
Vertex and Edge RDDs
Graph System Optimizations
Built-in Algorithms
Quiz
Summary
Summary (contd.)
Conclusion

Admission details

You can enrol for the Apache Spark and Scala Certification Training course by Edureka on the edureka website by making online payment using any of the following options:

Visa Credit or Debit Card
MasterCard
American Express
Diner’s Club
PayPal

Once payment is received, you will automatically receive a payment receipt and access information via email.

Filling the form

Follow the steps mentioned below to enroll for Apache Spark and Scala Certification Training

Step 1 - Click on the link - https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training

Step 2 - Click Enroll now option mentioned top on the right side. You will be redirected to a new page

Step 3 - If applicants have a coupon then they need to apply this or click on the Proceed button.

Step 4 - Provide necessary details i.e name, email, and contact number, and click on proceed

Step 5 - Pay the fee and save the receipt of the transaction for future reference.

Evaluation process

To become a certified Spark Developer, you need to pass the CCA175 Spark and Hadoop Developer exam. A total of 8-17 hands-on performance tasks will be assigned to the candidates. Candidates must score a minimum of 70% marks to pass the exam successfully.

In case a candidate fails the exam, they can reattempt the exam any number of times. However, once candidates clear the exam successfully, they are not allowed to retake it.

How it helps

With the Apache Spark market about to reach a market share of a whopping 4.2 Billion USD, now is an opportune time for interested candidates to train themselves in the language of Spark and Scala. With a growth rate of a respectable 67%, professionals will find the market strewn with lucrative job opportunities in sectors like manufacturing, retail, and healthcare.

Once they complete the Apache Spark and Scala Certification course, they can work as a Big Data Architect, Big Data Engineer, or Big Data Developer in varied sectors like IT, finance, healthcare, and many more. Besides, many companies like Amazon, Accenture, Visa, and more are always on the lookout to hire certified Big Data professionals.

FAQs

What is the validity of the Certification?

The Apache Spark and Scala Certification has lifelong validity.

How many marks do I need to pass the CCA175 exam?

In the CCA175 exam, candidates need to perform 8-12 hands-on tasks successfully. A candidate must score minimum 70% to pass the exam.

What does the CCA175 Hadoop certification exam cost?

The CCA175 Spark and Hadoop Developer exam costs 295 USD.

How many attempts can I take to pass the CCA175 exam?

Candidates can take the exam any number of times as they want until you pass. However, retakes are not allowed after the successful completion of a test.

How can I become a Spark Developer?

To become a certified Spark Developer, you need to pass the CCA175 Spark and Hadoop Developer exam and be proficient in the cleaning, analysis, and transformation of Spark.

Which companies hires a certified Spark Developer?

A certified Spark Developer is usually hired by companies such as Amazon, Accenture, Visa, Hewlett Packard Enterprises, Deloitte, etc.

Are there any prerequisites for the course?

To apply for the Apache Spark and Scala certification training course, interested candidates must have a fundamental knowledge of any programming language like Python, Java, etc. They should also have a basic understanding of SQL, database, and query languages for database. In addition, they should have a working knowledge of Linux and Unix-based systems.