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Intellipaat Spark training in India lets you master the real-time data processing using Spark Streaming, Spark SQL, Spark RDD and Spark Machine Learning libraries (Spark MLlib). Also, you will learn Scala programming as well as work on three real-life use cases. Get the best Spark and Scala course in India by the top Apache Spark Experts.
Intellipaat is leading online training provider bringing the most sought-after Apache Spark and Scala Course in India for Big Data aspirants of this country. Delivered by experienced trainers, this training program equips you with all the skills of Spark and Scala such as Spark RDDs and Dataframes, Scala Java interoperability, classes and traits, collections, etc.
In Intellipaat’s Apache Spark course, you will learn about:
Apache Spark training in India can be taken by:
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 India is ₹20650 INR and the duration of the CCA Spark and Hadoop Developer Exam (CCA175) is 120 minutes.
There is going to be a dearth of 200,000 Big Data Analysts in India.
India is one of the biggest locations across the globe investing huge amount of funds in Big Data Analytics. The aforementioned fact clarifies the situation that technologies like Apache Spark are much in demand in this market. Since Spark and Scala complement each other, this combination is quite popular in Indian firms creating huge career opportunities for candidates aspiring to become Spark and Scala professionals.
India is gradually emerging as a technology superpower attracting companies around the world to invest money in this market. These growth opportunities require extensive research and analytics by the firms through the tools like Apache Spark. Developing Apache Spark applications become easy with Scala. Hence, these two technologies are quite in demand in the Indian market.
Apache Spark helps you process streaming data in real time, while Scala helps you develop Spark applications with lesser lines of codes. Therefore, these two make a great technology combination. This Apache Spark and Scala training online provides you with hands-on experience in these two technologies through lab exercises and project works. Also, this course helps you prepare for the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.
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57% Average Salary Hike
$1,28,000 Highest Salary
12000+ Career Transitions
300+ Hiring Partners
Self Paced Training
Online Classroom Preferred
Module 01 - Introduction to ScalaPreview
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.
Module 02 - Pattern MatchingPreview
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
Module 03 - Executing the Scala CodePreview
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
Module 04 - Classes Concept in ScalaPreview
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
Module 05 - Case Classes and Pattern MatchingPreview
5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
Module 06 - Concepts of Traits with ExamplePreview
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
Module 07 - Scala–Java InteroperabilityPreview
7.1 Implementation of traits in Scala and Java
7.2 Handling of multiple traits extending
Module 08 - Scala CollectionsPreview
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
Module 09 - Mutable Collections Vs. Immutable CollectionsPreview
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
Module 10 - Use Case Bobsrockets PackagePreview
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
Module 11 - Introduction to SparkPreview
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 stack, 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
Module 12 - Spark BasicsPreview
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
Module 13 - Working with RDDs in SparkPreview
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
Module 14 - Aggregating Data with Pair RDDsPreview
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
Module 15 - Writing and Deploying Spark ApplicationsPreview
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
Module 16 - Parallel ProcessingPreview
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
Module 17 - Spark RDD PersistencePreview
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
Module 18 - Spark MLlibPreview
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
1. Building a Recommendation Engine
Module 19 - Integrating Apache Flume and Apache KafkaPreview
19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka
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
Module 20 - Spark StreamingPreview
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
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming
Module 21 - Improving Spark PerformancePreview
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
Module 22 - Spark SQL and Data FramesPreview
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
Module 23 - Scheduling/PartitioningPreview
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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Recommend the best movie based on the user's taste. This hands-on Apache Spark project, along with using the Apache Spark MLlib, includes the creation of collaborative filtering, regression, clustering, and dimensionality reduction.
Twitter API Integration for Tweet Analysis
This project facilitates learning to analyze the sentiments of the user by a tweet. As a part of the project, the learners will be required to successfully integrate Twitter API and utilize PHP or Python to build a server-side code.
Data Exploration Using Spark SQL – Wikipedia Data
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs.
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!
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.
I am glad that I took the Intellipaat Spark training online. The trainers offered quality training with real-world examples, and there was extensive interactivity throughout the training that made the Intellipaat training the best according to me.
I firmly believe that Intellipaat is the perfect place to embark on a great professional career in the technology space. Their Apache Spark online course was praiseworthy.
This course delivered everything. This online training course from Intellipaat is exactly what I wanted to understand Apache Spark and Scala in order to appear for the certification exam.
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.
A properly structured training that is simple to learn. Quality content
All videos are in-depth yet concise. I had no problem understanding the tough concepts. Wonderful job Intellipaat!
You have been extremely helpful for making me understand all demanding big data technologies at one place.
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.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
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 is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program 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.