Apache Spark and Scala Certification Training

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EdUnbox’s Spark and Scala certification training will advance your career and build your expertise. Additionally, this course will enable students to master essential and important skills of Spark OSS framework, as well as the Scala programming languages, such as Spark SQL, Spark Streaming, MLlib, Shell Scripting Spark, Spark RDD, and GraphX programming besides Messaging System like Kafka.

About Spark Scala Training Course:

EdUnbox offers students a Spark and Scala Training course to help them master real-time data processing deploying Scala and Spark Streaming. This course has been designed by Spark and Scala experts. Spark ML Libraries, Spark SQL, and Spark RDD are some of the key highlights of this course. Students will learn key skills of Apache Spark framework with Scala programming and essential skills for an enriching career.

What will you learn in this Apache Spark and Scala Training course?

EdUnbox’s Apache Spark and Scala Certification Training Course is designed to provide students with the skills and knowledge to evolve as successful developers This comprehensive course will cover topics listed below:

  • How Spark enables the in-memory data processing
  • Comparison of Spark with Hadoop MapReduce.
  • In-depth data on RDDs
  • RDD and its operation, plus a detailed understanding of Spark algorithm
  • Spark SQL for structured data processing
  • Various APIs offered by Spark such as Spark Streaming, and Spark MLlib.
  • Understand Spark Streaming, Spark MLilb
  • Explain GraphX programming and Shell Scripting Spark
  • Writing Spark applications in Python, Scala, and Java
  • Scala classes concepts, as well as executing pattern matching
  • Scala–Java interoperability and additional Scala operations
  • Working projects using Scala to run on Spark applications

 

Who should go for this Spark Scala training?

EdUnbox’s Apache Spark & Scala training course is excellent for advancing your career in Big Data Analytics, whether you want to be a developer or an engineer at a big MNC or private company. Here’s who will benefit from our course:

  • Developers and Architects, BI /ETL/DW Professionals
  • IT, Mainframe & Testing Professionals
  • Freshers and Big Data Enthusiasts
  • Data Scientists and Analytics Professional

What are the prerequisites for this Spark Scala training?

  • The most fundamental requirement for Apache Spark and Scala Online Course is a passion to learn.
  • Knowledge of programming basics would be a further added advantage.
  • Knowledge of database basics and a query language can help you in this course.
  • Knowledge of working of Unix or Linux also adds value.

Market Demand:

By completing this Apache Spark with Scala Online course for training experts, students will be able to access a lot of benefits:

  • Forbes estimates 56% of the enterprises will raise investment in big data over the coming three years
  • McKinsey forecasts that by 2018, there will be a shortfall of 1.5 million data experts
  • Average Salary that Spark Developers earn is $113k.
  • McKinsey further estimates the US alone will deal with a shortage of nearly 190,000 data scientists

 


1. An Introduction to Big Data, Hadoop and Spark

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

2. Introduction to Scala for Spark

  • What is Scala?
  • Why Scala for Spark?
  • Variable Types in Scala
  • Scala in other Frameworks
  • Introduction to Scala REPL
  • Hands-on: Scala REPL Detailed Demo
  • For each loop, Functions, and Procedures
  • Control Structures in Scala
  • Collections in Scala- Array
  • Basic Scala Operations
  • ArrayBuffer, Map, Tuples, Lists, and more

3. Functional Programming +OOPs Concepts in Scala

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

4. Scala For Beginners

  • Introducing Scala
  • Deployment of Scala for Big Data applications
  • Scala REPL
  • Lazy Values and Control Structures in Scala
  • Directed Acyclic Graph (DAG)
  • First Spark Application Using SBT/Eclipse
  • Spark Web UI and Spark in the Hadoop Ecosystem.
  • Pattern Matching
  • The Value of Scala
  • Concept of REPL (Read Evaluate Print Loop)
  • Deep dive into Scala pattern matching
  • Type interface, higher-order function and currying
  • Traits, application space and Scala for data analysis

5. Scala Concepts

  • Executing the Scala Code
  • Learning about the Scala Interpreter
  • Static object timer in Scala
  • Testing string equality in Scala
  • Implicit classes in Scala
  • Concept of currying in Scala
  • Numerous classes in Scala
  • Classes Concept in Scala
  • Understanding the constructor overloading
  • Different abstract classes
  • Hierarchy types in Scala
  • Object equality and the val and var methods in Scala
  • Case Classes and Pattern Matching
  • Understanding sealed traits, wild, constructor
  • Explanation of tuple, variable pattern, and constant pattern
  • Concepts of Traits with Example
  • Linearization of traits
  • Java equivalent and avoiding boilerplate code

6. Scala–Java Interoperability

  • Implementation of traits in Scala and Java
  • Handling of multiple traits extending
  • Scala Collections Difference between Iterator and Iterable in Scala
  • List sequence in Scala
  • Mutable Collections Vs. Immutable Collections
  • Understanding Lists and Arrays in Scala
  • Queue in Scala and double-ended queue Deque
  • Stacks, Sets, Maps, and Tuples in Scala
  • Introduction to Scala packages and imports
  • Selective imports
  • Scala test classes
  • Introduction to JUnit test class
  • JUnit interface through the JUnit 3 suite for the Scala tests
  • Packaging of Scala applications in Directory Structure
  • Examples of Spark Split and Spark Scala

7. Introduction to Spark

  • What is Spark?
  • Ways in Which Spark outperforms MapReduce
  • Understanding in-memory MapReduce
  • Interactive operations on MapReduce
  • Spark stack, fine vs. coarse-grained update
  • Spark stack, Spark Hadoop YARN
  • HDFS Revision and YARN Revision, Spark history server and Cloudera distribution

8. Fundamentals of Spark

  • Spark installation guide
  • Spark configuration and memory management
  • Executor memory vs. driver memory
  • Working with Spark Shell
  • Concept of resilient distributed datasets (RDD)
  • Learning to do functional programming in Spark
  • Architecture of Spark
  • Working with RDDs in Spark and creating RDDs
  • RDD partitioning, operations, and transformation in RDD
  • Deep dive into Spark RDDs
  • RDD general operations and pair RDD functions
  • Aggregating Data with Pair RDDs
  • Understanding the concept of Key-Value pair in RDDs
  • Learning how Spark makes MapReduce operations faster
  • Various operations of RDD

9. Writing and Deploying Spark Applications

  • Comparing the Spark applications with Spark Shell
  • Creating a Spark application using Scala or Java
  • Deploying a Spark application
  • Scala built application
  • Creation of mutable list, set and set operations
  • List, tuple, concatenating a list
  • Creating an application using SBT
  • Deploying the application using Maven
  • Configuring Spark

10. Parallel Processing

  • Spark parallel processing
  • Deploying on a cluster
  • Introduction to Spark partitions
  • File-based partitioning of RDDs
  • Understanding of HDFS and data locality
  • Mastering the technique of parallel operations
  • Comparing repartition and coalesce and RDD actions

11. Spark RDD Persistence & MLlib

  • Execution flow in Spark
  • RDD persistence overview
  • Spark execution flow and Spark terminology
  • Distribution shared memory vs. RDD
  • RDD limitations, Spark shell arguments
  • Distributed persistence
  • RDD lineage and Key-Value pair Spark MLlib Introduction
  • What is Machine Learning?
  • Kinds of Machine learning
  • Introduction to MLlib
  • Numerous ML algorithms supported by MLlib
  • Linear Regression
  • Logistic Regression
  • Decision Tree, Random Forest
  • K-means clustering techniques
  • Building a Recommendation Engine

12. Deep Dive into Spark Framework

  • Writing your first Spark Job Using SBT
  • Submitting Spark Job
  • Spark’s Place in the Hadoop Ecosystem
  • Spark Web UI
  • Spark Deployment Modes
  • Spark Components & its Architecture
  • Spark Application Web UI
  • Introduction to Spark Shell
  • Data Ingestion using Sqoop
  • Data ingestion using Sqoop
  • Configuring Spark Properties
  • Building and Running Spark Application

13. Spark RDDs

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

14. Apache Spark Framework

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

15. Spark Machine Learning and Deep Dive into MLlib Framework

  • Why Machine Learning?
  • Where Machine Learning is Used?
  • What is Machine Learning?
  • Various ML algorithms supported by MLlib
  • K-Means Clustering & How It Works with MLlib
  • Linear Regression, Logistic Regression, Decision Tree, Random Forest
  • Random Forest
  • Machine Learning MLlib
  • Logistic Regression
  • Linear Regression
  • K- Means Clustering
  • Introduction to MLlib
  • Features of MLlib and MLlib Tools
  • Face Detection: USE CASE
  • Different Types of Machine Learning Techniques
  • Decision Tree
  • Analysis of Data using MLlib (K-Means)

16. Apache Kafka and Apache Flume

  • Why Kafka
  • What is Kafka, Kafka architecture
  • Kafka workflow, configuring Kafka cluster
  • Basic operations
  • Kafka monitoring tools
  • Integration for Apache Flume and Apache Kafka
  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi-Broker Cluster
  • Producing and consuming messages
  • Integrating Apache Flume and Apache Kafka
  • Where is Kafka Used?
  • What is Apache Flume?
  • Understanding the Components of Kafka Cluster
  • The need for Apache Flume
  • Need for Kafka
  • Kafka Producer and Consumer Java API
  • Kafka Architecture
  • Integrating Apache Flume and Apache Kafka
  • Flume Sources
  • Flume Sinks
  • Flume Configuration
  • Flume Channels
  • Core Concepts of Kafka
  • Configuring Kafka Cluster
  • Basic Flume Architecture
  • Streaming Twitter Data into HDFS
  • Setting up Flume Agent
  • Producing and consuming messages
  • Flume Commands
  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi-Broker Cluster

17. Apache Spark Streaming – Data Sources

  • Introduction to Spark Streaming
  • Features of Spark Streaming, Spark Streaming workflow
  • Initializing Streaming Context
  • Discretized Streams (DStreams), Input DStreams and Receivers
  • Transformations on DStreams
  • Output Operations on DStreams, Windowed Operators

18. Improving Spark Performance

  • Spark Variables
  • Accumulators
  • Performance issues Troubleshooting

19 Scheduling/Partitioning

  • Spark scheduling and partitioning
  • Hash partition
  • Range partition
  • Scheduling within and around applications
  • Static partitioning, dynamic sharing, and fair scheduling
  • Map partition with index
  • Zip, GroupByKey, and Spark master high availability
  • Standby masters with ZooKeeper
  • Single-node Recovery with Local File Systems
  • Single-node Recovery With High Order Functions

EdUnbox is delighted to offer this comprehensive course for clearing the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Now, get the best positions in private companies, MNCs and PSUs with this useful qualification. As part of the training, we are also offering real-time assignments and projects that have amazing implications in the real-world industry scenario helping to accelerate your career effortlessly.
Towards the completion of this training program, you will participate in the real-time projects and quizzes that will prepare you for questions in the certification examination and help you to score well in this exam. EdUnbox Course Completion Certificate will be awarded on the completion of projects on the basis of trainer reviews and on scoring 50% minimum marks in the quiz.

EdUnbox certification is well recognized among leading corporate brands in a wide range of industries and verticals including Fortune 500 companies. Let the community know about your achievement and become certified today! Advance your career with our Apache Spark with Scala Training course.

We are offering Live Online Instructor-Led WebEx Training. Live Online Instructor-Led WebEx Training: Online training is conducted via live webex streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do provide recordings of each session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.
Your learning will be monitored by Our LMS. In case you are not able to attend any lecture, you can view the recorded session of the class in EdUnbox’s Learning Management System (LMS). To make things better for you, we also provide the facility to attend the missed session in any other live batch.
EdUnbox certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real life projects you have delivered.
All the instructors at EdUnbox are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by EdUnbox for providing an awesome learning experience to the participants.
Yes, we have group discount options for our training programs.

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

  • Visa Credit or Debit Card
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Spark training at Edunbox ticked all the right boxes. I liked about the Apache Spark certification training was the opportunity to work on real-world projects. Thank you Edunbox.

ravish kumar


great learning experience. Thank you so much edunbox.

aaryan


I did a lot of research for learning apache spark and landed on Edunbox and started learning. The experience was amazing and the trainer had good enough knowledge on the subject.

mahesh joshi


A properly structured training that is simple to learn. Quality content

ravikant


This course delivered everything. This online training course from Edunbox is exactly what I wanted to understand Apache Spark and Scala in order to appear for the certification exam.

samiksha


The quality of the course content is just awesome. Very happy to choose the right course for the career. Overall, a great set of tutorials.

hitesh pareek


The Edunbox Spark trainers were of the highest experience and knowledge. But what I liked was that the trainers went out their way in order to explain things and that too with real world examples which helped to learn Spark quickly. If I did not understand the first time they had the patience to explain it again making this learning experience one of a kind. The entire training is in line with clearing the Apache Spark Certification.

Anil Jain


I am glad that I took the Edunbox Spark training. The trainers offered quality Spark training with real-world examples, and there was extensive interactivity throughout the training that made the Edunbox training the best according to me.

shubhash


The course curriculum is very well designed. My doubts are always replied by the support team within 24 hrs. Thanks Team Edunbox.

amila kaur


The Edunbox Apache Spark training and support were really excellent. Due to this there was no impediment to my learning process.

alok beniwal


I believe that Edunbox is the perfect place to embark on a great professional career in Apache spark and scala. Their Apache Spark and Scala training course was good enough to get a dream job. Thanks

Abey thomas


All videos are in-depth yet concise. I had no problem understanding the tough concepts. Wonderful job Edunbox!

Prakaram singh


You have been extremely helpful for making me understand all demanding big data technologies at one place.

Surendra Haritwal


I have had a great experience with Edunbox learning about the newest technologies. The Apache Spark and Scala Certification Training has excellent course material, free tutorial content and videos. Edunbox you are amazing

arif khan


They promised that they will resolve my queries in less than 24 hours that’s exactly they did it. Thanks Edunbox

alvish

I am really grateful to Edunbox for this Apache Spark certification training.Great going Edunbox.

bhuwan



 

Course Curriculum

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Live Training

28
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18,000.00 13,500.00

05
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18,000.00

Key Feature

All the courses are instructor led training sessions. We also provide you all the resources that are required to complete your training including video, course material, exercise files and data sets used during the session.
Each module will be followed by practical assignments and lab exercises to exercise your learning . Towards the end of the course, you will be working on a project where you be expected to create a project based on your learning . Our support team is available to help through email, phone or Live Support for any help you require during Lab and Project work.
At end of training we will provide you EdUnbox Course completion Certificate. EdUnbox enjoys strong relationships with multiple companies across the globe. If you are looking out for exploring job opportunities, you can pass your resumes once you complete the course and we will help you with job assistance. We don’t charge any extra fees for passing the resume to our partners and clients.
EdUnbox courses come with lifetime free upgrade to latest version. It’s a lifetime investment in the skills you want to enhance
EdUnbox courses come with lifetime support. Our Support ensures that all your doubts and problems faced during labs and project work are clarified round the clock.

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