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Big Data Hadoop Course in Bangalore

5 ( 1,044 ) Ratings

Intellipaat’s Big Data course in Bangalore lets you master Big Data Hadoop and Spark online to get ready for the Cloudera CCA Spark and Hadoop Developer Certification (CCA175) exam. You will also learn Hadoop administration with 14 real-time industry-oriented case-study projects. Get the best Hadoop training in Bangalore from certified mentors.

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Key Highlights

60 Hrs Instructor Led Training
80 Hrs Self-paced Videos
120 Hrs Project & Exercises
Certification
Job Assistance
Flexible Schedule
Lifetime Free Upgrade
Mentor Support
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Big Data Hadoop Course in Bangalore Overview

What will you learn in this Big Data course in Bangalore?

  • Complete mastery of HDFS, MapReduce and Big Data Analytics
  • Deploying Hadoop clusters, managing, monitoring, scheduling and troubleshooting them
  • Hands-on experience of Hadoop Hive, HBase, Pig, Oozie, Sqoop, Flume and ZooKeeper
  • Spark and Storm and how to write applications in Python, Java and Scala
  • ETL connectivity with MapReduce, Hadoop testing using MRUnit and more

Software Developers, Mainframe, Analytics, Data warehousing, BI Professionals, and more

There is no prerequisite to learn Big data and Hadoop. Intellipaat provides the complimentary Java and Linux courses along with this course.

Bangalore is home to some of the best IT companies, and due to this the demand for Big Hadoop professionals is at an all-time high. This combined with the number of startups mushrooming in the Silicon Valley of India clarifies that the future for Hadoop can only get better.

The average salary of a Cloudera Certified Hadoop Developer in Bangalore is ₹1,700,000 per year – Indeed

Demand for Data Scientists in India is up by over 400% – Talent Supply Index

When it comes to Hadoop deployment in India, Bangalore is head and shoulders above the rest of the cities as shown in the following graph.

Demand for Data Scientists in India
Some of Bangalore’s top companies such as Flipkart, Ola, Infosys, etc., including the MNCs like IBM, have made some significant investments in Big Data Hadoop proving that this boom is here to stay, putting Bangalore at the top slot when it comes to Hadoop deployment.

  • Global Hadoop market to reach US$84.6 billion in 2 years – Allied Market Research
  • The number of jobs for all the US Data Professionals increases 2.7 million per year – IBM
  • A Hadoop Administrator in the United States gets a salary of US$123,000 – Indeed

According to the research firm McKinsey, there will be a shortage of 1.4–1.9 million Hadoop Data Analysts in the United States alone in the next 2 years. Big Data Hadoop is finding increased deployment by companies regardless of their industry orientation and customer segmentation. Due to this, there is a huge demand for professionals with the right set of skills and professional certification. This is where Intellipaat training course can make a difference to your career, thanks to its course material that is industry-oriented and in line with clearing the Cloudera certification.

Intellipaat stresses more on real-world projects and, thus, as part of this training course you will be exclusively working on 14 real-life Big Data Hadoop projects containing 70 datasets with over 1 billion data points.

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Information is the oil of the 21st century, and analytics is the combustion engine - Peter Sondergaard, Gartner
The global Hadoop Big Data analytics market size is expected to grow from USD 12.8 billion in 2020 to USD 23.5 billion by 2025. - MarketsandMarkets

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Skills Covered

Spark

Scala

Sqoop

Pig

Apache Flume

Hive

HCatalog

AVRO

Scala REPL

SBT/Eclipse

Apache Kafka

Spark Streaming

Impala

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Course Fees

Self Paced Training

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

$264

Online Classroom Preferred

  • Everything in Self-Paced Learning, plus
  • 60 Hrs of Instructor-led Training
  • One to one doubt resolution sessions
  • Attended as many batches as you want for Lifetime
  • Job Assistance
23 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

26 Oct

TUE - FRI

07:00 AM TO 09:00 AM IST (GMT +5:30)

30 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

07 Nov

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$ 449 $399 10% OFF Expires in

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  • Enterprise grade learning management system (LMS)
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Big Data Hadoop Course Content in Bangalore

Live Course Self Paced

Module 01 - Hadoop Installation and Setup

Preview

1.1 The architecture of Hadoop cluster
1.2 What is High Availability and Federation?
1.3 How to setup a production cluster?
1.4 Various shell commands in Hadoop
1.5 Understanding configuration files in Hadoop
1.6 Installing a single node cluster with Cloudera Manager
1.7 Understanding Spark, Scala, Sqoop, Pig, and Flume

Module 02 - Introduction to Big Data Hadoop and Understanding HDFS and MapReduce

Preview

2.1 Introducing Big Data and Hadoop
2.2 What is Big Data and where does Hadoop fit in?
2.3 Two important Hadoop ecosystem components, namely, MapReduce and HDFS
2.4 In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager

Hands-on Exercise:

1. HDFS working mechanism
2. Data replication process
3. How to determine the size of the block?
4. Understanding a data node and name node

3.1 Learning the working mechanism of MapReduce
3.2 Understanding the mapping and reducing stages in MR
3.3 Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort

Hands-on Exercise:

1. How to write a WordCount program in MapReduce?
2. How to write a Custom Partitioner?
3. What is a MapReduce Combiner?
4. How to run a job in a local job runner
5. Deploying a unit test
6. What is a map side join and reduce side join?
7. What is a tool runner?
8. How to use counters, dataset joining with map side, and reduce side joins?

4.1 Introducing Hadoop Hive
4.2 Detailed architecture of Hive
4.3 Comparing Hive with Pig and RDBMS
4.4 Working with Hive Query Language
4.5 Creation of a database, table, group by and other clauses
4.6 Various types of Hive tables, HCatalog
4.7 Storing the Hive Results, Hive partitioning, and Buckets

Hands-on Exercise:

1. Database creation in Hive
2. Dropping a database
3. Hive table creation
4. How to change the database?
5. Data loading
6. Dropping and altering table
7. Pulling data by writing Hive queries with filter conditions
8. Table partitioning in Hive
9. What is a group by clause?

5.1 Indexing in Hive
5.2 The ap Side Join in Hive
5.3 Working with complex data types
5.4 The Hive user-defined functions
5.5 Introduction to Impala
5.6 Comparing Hive with Impala
5.7 The detailed architecture of Impala

Hands-on Exercise: 

1. How to work with Hive queries?
2. The process of joining the table and writing indexes
3. External table and sequence table deployment
4. Data storage in a different table

6.1 Apache Pig introduction and its various features
6.2 Various data types and schema in Hive
6.3 The available functions in Pig, Hive Bags, Tuples, and Fields

Hands-on Exercise: 

1. Working with Pig in MapReduce and local mode
2. Loading of data
3. Limiting data to 4 rows
4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive

7.1 Apache Sqoop introduction
7.2 Importing and exporting data
7.3 Performance improvement with Sqoop
7.4 Sqoop limitations
7.5 Introduction to Flume and understanding the architecture of Flume
7.6 What is HBase and the CAP theorem?

Hands-on Exercise: 

1. Working with Flume to generate Sequence Number and consume it
2. Using the Flume Agent to consume the Twitter data
3. Using AVRO to create Hive Table
4. AVRO with Pig
5. Creating Table in HBase
6. Deploying Disable, Scan, and Enable Table

8.1 Using Scala for writing Apache Spark applications
8.2 Detailed study of Scala
8.3 The need for Scala
8.4 The concept of object-oriented programming
8.5 Executing the Scala code
8.6 Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
8.7 The Java and Scala interoperability
8.8 The concept of functional programming and anonymous functions
8.9 Bobsrockets package and comparing the mutable and immutable collections
8.10 Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.

Hands-on Exercise:

1. Writing Spark application using Scala
2. Understanding the robustness of Scala for Spark real-time analytics operation

9.1 Introduction to Scala packages and imports
9.2 The selective imports
9.3 The Scala test classes
9.4 Introduction to JUnit test class
9.5 JUnit interface via JUnit 3 suite for Scala test
9.6 Packaging of Scala applications in the directory structure
9.7 Examples of Spark Split and Spark Scala

10.1 Introduction to Spark
10.2 Spark overcomes the drawbacks of working on MapReduce
10.3 Understanding in-memory MapReduce
10.4 Interactive operations on MapReduce
10.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
10.6 The overview of Spark and how it is better than Hadoop
10.7 Deploying Spark without Hadoop
10.8 Spark history server and Cloudera distribution

11.1 Spark installation guide
11.2 Spark configuration
11.3 Memory management
11.4 Executor memory vs. driver memory
11.5 Working with Spark Shell
11.6 The concept of resilient distributed datasets (RDD)
11.7 Learning to do functional programming in Spark
11.8 The architecture of Spark

12.1 Spark RDD
12.2 Creating RDDs
12.3 RDD partitioning
12.4 Operations and transformation in RDD
12.5 Deep dive into Spark RDDs
12.6 The RDD general operations
12.7 Read-only partitioned collection of records
12.8 Using the concept of RDD for faster and efficient data processing
12.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

13.1 Understanding the concept of key-value pair in RDDs
13.2 Learning how Spark makes MapReduce operations faster
13.3 Various operations of RDD
13.4 MapReduce interactive operations
13.5 Fine and coarse-grained update
13.6 Spark stack

14.1 Comparing the Spark applications with Spark Shell
14.2 Creating a Spark application using Scala or Java
14.3 Deploying a Spark application
14.4 Scala built application
14.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
14.6 Creating an application using SBT
14.7 Deploying an application using Maven
14.8 The web user interface of Spark application
14.9 A real-world example of Spark
14.10 Configuring of Spark

15.1 Working towards the solution of the Hadoop project solution
15.2 Its problem statements and the possible solution outcomes
15.3 Preparing for the Cloudera certifications
15.4 Points to focus on scoring the highest marks
15.5 Tips for cracking Hadoop interview questions

Hands-on Exercise:

1. The project of a real-world high value Big Data Hadoop application
2. Getting the right solution based on the criteria set by the Intellipaat team

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

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

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

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

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

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

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

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

Following topics will be available only in self-paced mode:

24.1 Create a 4-node Hadoop cluster setup
24.2 Running the MapReduce Jobs on the Hadoop cluster
24.3 Successfully running the MapReduce code
24.4 Working with the Cloudera Manager setup

Hands-on Exercise:

1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
2. Working with the Cloudera Manager

25.1 Overview of Hadoop configuration
25.2 The importance of Hadoop configuration file
25.3 The various parameters and values of configuration
25.4 The HDFS parameters and MapReduce parameters
25.5 Setting up the Hadoop environment
25.6 The Include and Exclude configuration files
25.7 The administration and maintenance of name node, data node directory structures, and files
25.8 What is a File system image?
25.9 Understanding Edit log

Hands-on Exercise:

1. The process of performance tuning in MapReduce

26.1 Introduction to the checkpoint procedure, name node failure
26.2 How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes

Hands-on Exercise:

1. How to go about ensuring the MapReduce File System Recovery for different scenarios
2. JMX monitoring of the Hadoop cluster
3. How to use the logs and stack traces for monitoring and troubleshooting
4. Using the Job Scheduler for scheduling jobs in the same cluster
5. Getting the MapReduce job submission flow
6. FIFO schedule
7. Getting to know the Fair Scheduler and its configuration

27.1 How ETL tools work in Big Data industry?
27.2 Introduction to ETL and data warehousing
27.3 Working with prominent use cases of Big Data in ETL industry
27.4 End-to-end ETL PoC showing Big Data integration with ETL tool

Hands-on Exercise:

1. Connecting to HDFS from ETL tool
2. Moving data from Local system to HDFS
3. Moving data from DBMS to HDFS,
4. Working with Hive with ETL Tool
5. Creating MapReduce job in ETL tool

28.1 Importance of testing
28.2 Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing

29.1 Understanding the Requirement
29.2 Preparation of the Testing Estimation
29.3 Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure
29.4 Consolidating all the defects and create defect reports
29.5 Validating new feature and issues in Core Hadoop

30.1 Report defects to the development team or manager and driving them to closure
30.2 Consolidate all the defects and create defect reports
30.3 Responsible for creating a testing framework called MRUnit for testing of MapReduce programs

31.1 Automation testing using the OOZIE
31.2 Data validation using the query surge tool

32.1 Test plan for HDFS upgrade
32.2 Test automation and result

33.1 Test, install and configure

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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!

Big Data Hadoop Certification in Bangalore

This training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exam. The entire training course content is in line with this exam and helps you clear it with ease and get the best jobs in the top MNCs.

As part of this Big Data course in Bangalore, you will be working on real-time projects and assignments that have immense implications in real-world industry scenarios, thus helping you fast-track your career effortlessly.

At the end of this Big Data Hadoop training in Bangalore, there will be quizzes that perfectly reflect the type of questions asked in the certification exam and help you score better.

Intellipaat’s course completion certificate will be awarded upon the completion of the project work (after expert review) and scoring at least 60 percent marks in the quiz. This certification is well-recognized by top 80+ MNCs such as Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

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Big Data Hadoop Training in Bangalore FAQs

Why should I join the Big Data Hadoop training in Bangalore at Intellipaat?

It is a known fact that the demand for Hadoop professionals far exceeds the supply. So, if you want to learn and make a career in Hadoop, then you need to enroll in Intellipaat’s Hadoop course online, which is the most recognized name in Hadoop training and certification.

Intellipaat’s Hadoop training includes all major components of Big Data and Hadoop, such as Apache Spark, MapReduce, HBase, HDFS, Pig, Sqoop, Flume, Oozie, and more. This Big Data training in Bangalore is designed and developed by industry professionals. You will get 24/7 lifetime support, high-quality course material and videos, and free upgrades of the course material. Thus, it is clearly a one-time investment for a lifetime of benefits.

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

Here is a list of all the areas where we provide our Hadoop training in Bangalore:

Area Pin Code
Basavanagudi 560004
Malleswaram 560003
Agram 560007
Fraser Town 560005
HAL 560008
J. C. Nagar 560006
Doorvaninagar 560016
NAL 560017
Jalahalli 560014
Gavipuram 560019
Chamrajpet 560018
Gayathrinagar 560021
Seshadripuram 560020
Yeshwanthpur 560022
Magadi Road 560023
Anandnagar 560024
Deepanjalinagar 560026
Sampangiramnagar 560027
Adugodi 560030
Dharmaram College 560029
Agara 560034
Amruthahalli 560092
Anekal 562106
Ashoknagar 560050
Attibele 562107
Attur 560064
Austin Town 560047
Bagalgunte 560073
Bagalur 562149
Banashankari 560070
Banaswadi 560043
Bannerghatta Road 560083
Bapagrama 560091
Basaveshwaranagar 560079
Begur 560068
Bellandur 560103
Benson Town 560046
Bettahalsur 562157
Bhattarahalli 560049
Bolare 560082
Bommasandra 560099
C. V. Raman Nagar 560093
Carmelaram 560035
Chamarajasagara 562120
Chandra Layout 560040
Chickpet 560053
Chikkabettahalli 560097
Chikkalasandra 560061
Chikkanahalli 562130
Chudenapura 560060
Devanagundi 560067
Devasandra 560036
Doddakallasandra 560062
Doddanekkundi 560037
Domlur 560071
Dommasandra 562125
Dr. Shivarama Karanth Nagar 560077
Electronics City 560100
G. K. V. K. 560065
Gunjur 560087
Hampinagar 560104
Haragadde 560105
Hoodi 560048
HSR Layout 560102
Immedihalli 560066
Indiranagar 560038
J. P. Nagar 560078
Jayanagar 560041
Jeevan Bhima Nagar 560075
Kacharakanahalli 560084
Kanakanagar 560032
Kanteeravanagar 560096
Kenchanahalli 560098
Koramangala 560095
Kumbalagodu 560074
Laggere 560058
Mahalakshmipuram Layout 560086
Mathikere 560054
Nagarbhavi 560072
Nayandahalli 560039
Peenya Dasarahalli 560057
RV Niketan 560059
Sadashivanagar 560080
Tarabanahalli 560090
Yelahanka 560063

Intellipaat has a plethora of courses that will help you become a Data Analyst. The comprehensive Data Scientist training course, Big Data, Python, Machine Learning, Data Science Master’s courses, and others will help you process, inspect, cleanse, transform, and create model data to gain useful information.

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.

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Our Bangalore Mailing Address

Big Data Hadoop Training Certification Course in Bangalore. Address – AMR Tech Park 3,Ground Floor Tower B Hongasandra Village, Hosur Rd, Bommanahalli, Bengaluru, Karnataka – 560068, India. Call Us: +91-7022374614 View Location

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