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Apache Spark and Scala Training in Bangalore

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Intellipaat Spark training in Bangalore lets you master real-time data processing using Spark Streaming, Spark SQL, RDD and Machine Learning libraries etc. with real-time projects to clear Cloudera Certification (CCA175) exam. Learn Spark and Scala programming from industry experts with the best Spark and Scala course in Bangalore.

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Apache Spark and Scala Training Key Highlights

24 Hrs Instructor Led Training
22 Hrs Self-paced Videos
60 Hrs Project & Exercises
Certification
Job Assistance
Flexible Schedule
Lifetime Free Upgrade
Mentor Support
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Spark and Scala Training in Bangalore Overview

What will you learn in this Apache Spark Training in Bangalore?

In Intellipaat’s Apache Spark course, you will learn about:

  1. Fundamentals of Apache Spark and Scala
  2. Difference between Spark and Hadoop
  3. Implementing Spark on a cluster
  4. Scala programming language and its concepts
  5. Writing Spark applications in Scala, Java and Python
  6. Scala­–Java interoperability

Apache Spark training can be joined by:

  • Software Engineers looking to upgrade Big Data skills
  • Data Engineers and ETL Developers
  • Data Scientists and Analytics Professionals
  • Graduates who are looking to make a career in Big Data

There are no prerequisites for learning Spark online. However, a basic knowledge of database, SQL and query language can help.

The cost of the Spark Certification Exam in Bangalore is ₹20650 INR and the duration of the CCA Spark and Hadoop Developer Exam (CCA175) is 120 minutes.

Bangalore is known to be the technology capital of India. This city consists of world’s top MNCs as well as small start-ups thriving in this market. This is the second city in the country after Mumbai paying the highest salaries to Big Data Analysts around Rs.10.3 lacs per annum. This strikingly high salary denotes that there is a dearth of quality Big Data professionals in this city. Hence, it is a golden opportunity for Spark and Scala professionals to kick-start their careers.

Big Data Analytics cannot be performed smoothly till it is accompanied by a strong technology to leverage its strength, and this is the reason why companies use Scala along with Apache Spark. Bangalore-based firms realize this necessity, and hence they are looking for skilled Spark and Scala professionals to perform crucial job roles.

The key components of Apache Spark are:

Component Description
Spark Core The core component of Spark provides the basic functionality for the distributed processing of data. It includes APIs for RDDs (Resilient Distributed Datasets), which are immutable collections of data that can be operated on in parallel.
Spark SQL A component that provides SQL and structured data processing capabilities on top of Spark Core. It allows users to query data from a variety of sources, including Hive, JSON, and JDBC.
Spark Streaming A component that allows Spark to process real-time data streams. It can be used to ingest data from a variety of sources, such as Kafka, Twitter, and sensors.
MLlib A machine learning library that provides a variety of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
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Spark and Kafka are powering today's modern data apps - Forbes
Spark can be 100x faster than Hadoop for large scale data processing - Databricks

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Skills Covered

Hadoop

Scala

Python

Java

MLlib

K-means clustering

Kafka

Flume

Hive

Spark SQL

Maven

Scala–Java

Cloudera

ZooKeeper

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Apache Spark and Scala Course Fees

Self Paced Training

  • 22 Hrs e-learning videos
  • Flexible Schedule
  • Lifetime Free Upgrade

$264

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Spark and Scala Course Curriculum in Bangalore

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Scala Course Content

Module 01 - Introduction to Scala

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1.1 Introducing Scala
1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
1.3 Scala REPL, lazy values, and control structures in Scala
1.4 Directed Acyclic Graph (DAG)
1.5 First Spark application using SBT/Eclipse
1.6 Spark Web UI
1.7 Spark in the Hadoop ecosystem.

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Module 02 - Pattern Matching

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2.1 The importance of Scala
2.2 The concept of REPL (Read Evaluate Print Loop)
2.3 Deep dive into Scala pattern matching
2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis

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3.1 Learning about the Scala Interpreter
3.2 Static object timer in Scala and testing string equality in Scala
3.3 Implicit classes in Scala
3.4 The concept of currying in Scala
3.5 Various classes in Scala

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4.1 Learning about the Classes concept
4.2 Understanding the constructor overloading
4.3 Various abstract classes
4.4 The hierarchy types in Scala
4.5 The concept of object equality
4.6 The val and var methods in Scala

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5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern

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6.1 Understanding traits in Scala
6.2 The advantages of traits
6.3 Linearization of traits
6.4 The Java equivalent
6.5 Avoiding of boilerplate code

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7.1 Implementation of traits in Scala and Java
7.2 Handling of multiple traits extending

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8.1 Introduction to Scala collections
8.2 Classification of collections
8.3 The difference between iterator and iterable in Scala
8.4 Example of list sequence in Scala

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9.1 The two types of collections in Scala
9.2 Mutable and immutable collections
9.3 Understanding lists and arrays in Scala
9.4 The list buffer and array buffer
9.6 Queue in Scala
9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala

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10.1 Introduction to Scala packages and imports
10.2 The selective imports
10.3 The Scala test classes
10.4 Introduction to JUnit test class
10.5 JUnit interface via JUnit 3 suite for Scala test
10.6 Packaging of Scala applications in the directory structure
10.7 Examples of Spark Split and Spark Scala

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Spark Course Content

11.1 Introduction to Spark
11.2 Spark overcomes the drawbacks of working on MapReduce
11.3 Understanding in-memory MapReduce
11.4 Interactive operations on MapReduce
11.5 Spark stack, fine vs. coarse-grained update,, Spark Hadoop YARN, HDFS Revision, and YARN Revision
11.6 The overview of Spark and how it is better than Hadoop
11.7 Deploying Spark without Hadoop
11.8 Spark history server and Cloudera distribution

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12.1 Spark installation guide
12.2 Spark configuration
12.3 Memory management
12.4 Executor memory vs. driver memory
12.5 Working with Spark Shell
12.6 The concept of resilient distributed datasets (RDD)
12.7 Learning to do functional programming in Spark
12.8 The architecture of Spark

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13.1 Spark RDD
13.2 Creating RDDs
13.3 RDD partitioning
13.4 Operations and transformation in RDD
13.5 Deep dive into Spark RDDs
13.6 The RDD general operations
13.7 Read-only partitioned collection of records
13.8 Using the concept of RDD for faster and efficient data processing
13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

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14.1 Understanding the concept of key-value pair in RDDs
14.2 Learning how Spark makes MapReduce operations faster
14.3 Various operations of RDD
14.4 MapReduce interactive operations
14.5 Fine and coarse-grained update
14.6 Spark stack

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15.1 Comparing the Spark applications with Spark Shell
15.2 Creating a Spark application using Scala or Java
15.3 Deploying a Spark application
15.4 Scala built application
15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
15.6 Creating an application using SBT
15.7 Deploying an application using Maven
15.8 The web user interface of Spark application
15.9 A real-world example of Spark
15.10 Configuring of Spark

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16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions

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17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

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18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Hands-on Exercise: 
1. Building a Recommendation Engine

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19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.5 Operations
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka

Hands-on Exercise: 
1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka

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20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators

Hands-on Exercise: 
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming

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21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems

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22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine

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23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

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Spark Course Projects

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

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Career Services

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up-skilling objective.

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

Mock Interview Preparation

After 80% of the course completion.

Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, experience, and future career aspirations.

Assured Interviews

After 80% of the course completion

Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Apache Spark Certification in Bangalore

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This course is designed for clearing the Apache Spark component of the Cloudera Spark and Hadoop Developer Certification (CCA175) exam. Check our Hadoop training course for gaining proficiency in the Hadoop component of the CCA175 exam. The complete course is created by industry experts for professionals to get top jobs in the best organizations. The entire training includes real-world projects and case studies that are highly valuable.

Upon the completion of the training, you will have quizzes that will help you prepare for the CCA175 certification exam and score top marks.

The Intellipaat certification is awarded upon successfully completing the project work and after its review by experts. The Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony and Ericsson, among others.

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FAQ’s on Apache Spark Online Training

Why should I learn Apache Spark and Scala Training in Bangalore from Intellipaat?

Intellipaat is the pioneer in Hadoop training in India. So, it pays to be with the market leader like Intellipaat to learn Spark and Scala and get the best jobs in top MNCs for top salaries. The Intellipaat training is the most comprehensive course that includes real-time projects and assignments which are designed by industry experts. The entire course content is fully aligned towards clearing the exam for the Apache Spark component of the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.

Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrades to the latest version at no extra fee. For Hadoop and Spark training, you get the Intellipaat Proprietary Virtual Machine for lifetime and free cloud access for 6 months for performing training exercises. Hence, it is clearly a one-time investment.

Intellipaat has been serving Spark enthusiasts from every corner of the city. You can be living in any locality in Bengaluru, be it Marathahalli, Koramangala, BTM Layout, Jayanagar, Sarjapur, Vijaynagar, Whitefield, HSR Layout, Indira Nagar, Electronic City, Richmond Town, Hebbal, Bellandur, Rajajinagar or anywhere. You can have full access to our Apache Spark online training sitting at home or office 24/7.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have successfully completed the training and moved to the placement pool after clearing the PRT( Placement Readiness Test) More than 500+ top MNC’s and startups hire Intellipaat learners. Our Alumni works with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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Bangalore Address

Spark Training in Bangalore. Address – 1st Floor, Khykha Court, No.5, I Cross, Madiwala, Hosur Main Road, Bangalore – 560068, India. Call Us: +91-7022374614

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