Apache Spark Training | Apache Spark Certification Course | Edureka

Apache Spark and Scala Certification Training


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Why should you take Apache Spark and Scala course ?

  • Apache Spark will dominate the Big Data landscape by 2022 - Wikibon
  • ​The average pay stands at 10​8,​366 USD p.a - ​Indeed.com​​
  • 23K + satisfied learners. Reviews
  • 60 days of   Cloud Lab   access
About the Course

Edureka’s Apache Spark and Scala Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Throughout the Apache Spark Training, you will get an in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes  Spark RDD, Spark SQL, Spark MLlib and Spark Streaming.You will also get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka.

Instructor-led Apache Spark and Scala live online classes

30 th  Nov
Fri & Sat(6 Weeks) Weekend Batch Timings : 09:30 PM - 12:30 AM (EST)
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15 th  Dec
Sat & Sun (6 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)
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23 rd  Dec
Sun - Thu (18 Days) Timings : 08:30 PM - 10:30 PM (EST)
12 th  Jan
Sat & Sun (6 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)
14 th  Jan
Mon - Fri (18 Days) Timings : 10:00 AM - 12:00 PM (EST)

Course Price

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    Learning Objectives: Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture and you will also get an introduction to Spark and you will get to know about the difference between batch processing and real-time processing.

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
  • Big Data Analytics with Batch & Real-time Processing
  • Why Spark is needed?
  • What is Spark?
  • How Spark differs from other frameworks?
  • Spark at Yahoo!
    Learning Objectives: Learn the basics of Scala that are required for programming Spark applications. You will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

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:
  • Scala REPL Detailed Demo
    Learning Objectives: In this module, you will learn about object-oriented programming and functional programming techniques 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:
  • OOPs Concepts
  • Functional Programming
    Learning Objectives: Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

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
    Learning Objectives: Get an insight of Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions and Functions performed on RDD).

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
    Learning Objectives: In this module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.

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
    Learning Objectives: Learn why machine learning is needed, different Machine Learning techniques/algorithms, and 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
    Learning Objectives: Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

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
    Learning Objectives: Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using 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
    Learning Objectives: Work on Spark streaming which is used to build scalable fault-tolerant streaming applications. Also, learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

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
    Learning Objectives: In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

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
    Learning Objectives: Work on an end-to-end Financial domain project covering all the major concepts of Spark taught during the course.
    Learning Objectives: In this module, you will be learning the key concepts of Spark GraphX programming and operations along with different GraphX algorithms and their implementations.
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Apache Spark Certification Training Course is designed to provide you with the knowledge and skills to become a successful Big Data & Spark Developer. This Training would help you to clear the CCA Spark and Hadoop Developer (CCA175) Examination.

You will understand the basics of Big Data and Hadoop. You will learn how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce. You will also learn about RDDs, Spark SQL for structured processing, different APIs offered by Spark such as Spark Streaming, Spark MLlib. This course is an integral part of a Big Data Developer’s Career path. It will also encompass the fundamental concepts such as data capturing using Flume, data loading using Sqoop, messaging system like Kafka, etc. 
Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The Spark Scala Course offers:
  • Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
  • Comprehensive knowledge of various tools that fall in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
  • The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
  • The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
  • The exposure to many real-life industry-based projects which will be executed using Edureka’s CloudLab
  • Projects which are diverse in nature covering banking, telecommunication, social media, and govenment domains
  • Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices

Spark is one of the most growing and widely used tool for Big Data & Analytics. It has been adopted by multiple companies falling into various domains around the globe and therefore, offers promising career opportunities. In order to take part in these kind of opportunities, you need a structured training that is aligned as per Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices.

Besides strong theoretical understanding, it is quite essential to have a strong hands-on experience. Hence, during the Edureka’s Spark and Scala course, you will be working on various industry-based use-cases and projects incorporating big data and spark tools as a part of solution strategy.

Additionally, all your doubts will be addressed by the industry professional, currently working on real life big data and analytics projects.

    The Edureka’s Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to-
  • Write Scala Programs to build Spark Application
  • Master the concepts of HDFS
  • Understand Hadoop 2.x Architecture
  • Understand Spark and its Ecosystem
  • Implement Spark operations on Spark Shell
  • Implement Spark applications on YARN (Hadoop)
  • Write Spark Applications using Spark RDD concepts
  • Learn data ingestion using Sqoop
  • Perform SQL queries using Spark SQL
  • Implement various machine learning algorithms in Spark MLlib API and Clustering
  • Explain Kafka and its components
  • Understand Flume and its components
  • Integrate Kafka with real time streaming systems like Flume
  • Use Kafka to produce and consume messages
  • Build Spark Streaming Application
  • Process Multiple Batches in Spark Streaming
  • Implement different streaming data sources
Market for Big Data Analytics is growing tremendously across the world and such strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few Professional IT groups, who are continuously enjoying the benefits and perks of moving into Big Data domain.
  • Developers and Architects
  • BI /ETL/DW Professionals
  • Senior IT Professionals
  • Testing Professionals
  • Mainframe Professionals
  • Freshers
  • Big Data Enthusiasts
  • Software Architects, Engineers and Developers
  • Data Scientists and Analytics Professionals
The stats provided below will provide you a glimpse of growing popularity and adoption rate of Big Data tools like Spark in the current as well as upcoming years:
  • 56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years – Forbes
  • McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
  • Average Salary of Spark Developers is $113k
  • According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018
  • As you know, nowadays, many organisations are showing interest in Big Data and are adopting Spark as a part of solution strategy, the demand of jobs in Big Data and Spark is rising rapidly. So, it is high time to pursue your career in the field of Big Data & Analytics with our Spark and Scala Certification Training Course.

Edureka’s Apache Spark and Scala Developer Certificate Holders work at 1000s of companies like

5000 Total number of reviews
4.57 Aggregate review score
80% Course completion rate
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    You will execute all your Spark and Scala Course Assignments/Case Studies on the Cloud LAB environment provided by Edureka. You will be accessing the Cloud LAB via browser. In case of any doubt, Edureka’s Support Team will be available 24*7 for prompt assistance.
    CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the Spark Training Course where you can execute all the in-class demos and work on real life spark case studies fluently. This will not only save you from the trouble of installing and maintaining Spark and Scala on a virtual machine, but will also provide you an experience of a real big data and spark production cluster. You’ll be able to access the Spark Training CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support team is ready to assist 24×7.
    You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's Spark Training.
    Project 1- Domain: Financial 
    Statement: A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, it makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

    Project 2- Domain: Transportation Industry
    Business challenge/requirement: With the spike in pollution levels and the fuel prices, many Bicycle Sharing Programs are running around the world. Bicycle sharing systems are a means of renting bicycles where the process of obtaining membership, rental and bike return is automated via a network of joint locations throughout the city. Using this system people can rent a bike from one location and return it to a different place as and when needed. 
    Considerations: You are building a Bicycle Sharing demand forecasting service that combines historical usage patterns with weather data to forecast the Bicycle rental demand in real-time. To develop this system, you must first explore the dataset and build a model. Once it’s done you must persist the model and then on each request run a Spark job to load the model and make predictions on each Spark Streaming request

Instructor-led Sessions

36 hrs of Online Live Instructor-led Classes. Weekend class:12 sessions of 3 hours each and Weekday class:18 sessions of 2 hours each.

Real-life Project

Towards the end of the course, you will be working on a Real Life project.

Assignments

Each class will be followed by practical case studies which can be completed before the next class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.

Certification

Towards the end of the course, you will be working on a project. Edureka certifies you as an Spark Expert based on the project.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

Cloud Lab New

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment
"You will never miss a lecture at Edureka! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch."

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

You can give us a CALL at +91 90660 20867/1844 230 6362 (US Tollfree Number) OR email at sales@edureka.co

You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
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  • Activate your loan: Setup automatic repayment using NACH to activate your loan
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