Big Data Hadoop Training Institute in Mumbai | Best Hadoop Course | ACTE
Home » Bi & Data Warehousing Courses India » Hadoop Training in Mumbai

Hadoop Training in Mumbai

(5.0) 6231 Ratings 6544 Learners

Live Instructor LED Online Training

Learn from Certified Experts

  • Shape your career as Big Data shapes the IT World.
  • Acquire an understanding of the ZooKeeper service.
  • Fully updated and industry-led course material.
  • Delivered by 9+ years of Hadoop Certified Expert.
  • Affordable Fees with Best curriculum Designed by Industrial Hadoop Expert.
  • Our Next Hadoop Batch to begin your tech week– Register Your Name Now!


INR 18000

INR 14000


INR 20000

INR 16000

Have Queries? Ask our Experts

+91-8376 802 119

Available 24x7 for your queries

Upcoming Batches


Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session


Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session


Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session


Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

Hear it from our Graduate

Learn at Home with ACTE

Online Courses by Certified Experts

Experts who practice in projects and find themselves in IT companies

  • The course will cover much more than Hadoop. In this course, you will learn how to install, configure, and handle big data.
  • This course will also demonstrate how the technologies can be applied to solve problems in the real world! There is only a requirement for familiarity with UNIX and Java.
  • Taking this course will equip you with the knowledge and confidence necessary to succeed at your job. You will gain the knowledge and skills to efficiently handle big data projects after completing this course.
  • We will be covering Apache Pig, HDFS, and MapReduce in this course. You can also create and configure EC2 and Hadoop instances. The examples, applications, and explanations you will find in this section are numerous.
  • In addition to taking theory courses, students should take practical courses as well. After graduating from our program, our graduates can find employment in a wide variety of industries.
  • The course teaches you how to integrate Hadoop into your everyday life as well as how to solve real-world problems with it. Additionally, a certificate will be given to you upon completion!
  • Concepts: High Availability, Big Data opportunities, Challenges, Hadoop Distributed File System (HDFS), Map Reduce, API discussion, Hive, Hive Services, Hive Shell, Hive Server and Hive Web Interface, SQOOP, H Catalogue, Flume, Oozie.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

This is How ACTE Students Prepare for Better Jobs


Course Objectives

Hadoop is an Apache project to store and process Big Data. Hadoop stores Big Data over commodity hardware in a distributed and fault-tolerant way. Hadoop's tools are subsequently used to parallel HDFS data processing.Because companies have realized the advantages of Big Data Analytics, big data & shadow professionals are in demand. Big data & Hadoop experts with Hadoop Ecosystem knowledge and best practice on HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop, and Flume are sought by companies.
This course provides you with information on the Hadoop ecosystem and big-data tools and methodologies to prepare you to be a big-data engineer and to complete your role. Your new big data skills and on-the-job expertise are demonstrated with the course certification. Hadoop certification will educate you into ecosystem instruments like Hadoop, HDFS, MapReduce, Flume, Kafka, Hive, HBase, etc.
  • Hadoop and YARN fundamentals and write applications
  • Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications HDFS MapReduce, Hive, Pig, Sqoop, Flume, and ZooKeeper Spark
  • Avro data formats work
  • Use Hadoop and Apache Spark to implement real-life projects
  • Be prepared to clear the certification with Big Data Hadoop.
  • System administrators and programming developers
  • Trade and project managers with experienced experience
  • Big Data Hadoop Developers want to learn other vertical elements like testing, analysis, and management.
  • Professional mainframes, architects, and experts in testing
  • Professionals in business intelligence, data warehousing, and analysis
  • Graduates will want to learn Big Data.
    This Big Data class and master Hadoop are not subject to requirements. But UNIX, SQL, Java, and Big Data Hadoop are all basics.
Big Data is the fastest growing and most promising technology for the treatment of large amounts of data. This Big Data Hadoop training helps you to achieve the highest professional qualifications. Nearly every top MNC tries to enter Big Data Hadoop, which makes it very necessary for certified Big Data professionals to work.
Big Data Hadoop Certification training is designed to make you a Certified Big Data Practitioner by industry experts. The course of Big Data Hadoop:
  • In-depth knowledge of the HD FS, YARN (Another Resource Negotiator) & Map Big Data, and Hadoop including the HDFS Cutting.
  • Comprehensive knowledge of various tools such as Pig, Hive, Sqoop, Flume, Oozie, and HBase that fall within Hadoop Ecologic.
  • The ability to integrate HDFS data using Sqoop and Flume and to analyze large HDFS-based datasets of a diverse nature covering several data sets from multiple fields such as banking, tea, and so on.

What are our Big Data Hadoop Certification Training capabilities you will learn?

The certification training in Big Data Hadoop will help you to become a Big Data expert. It will enhance your skills by providing you with comprehensive expertise in Hadoop and the practical experience required to solve projects based in the industry in real-time.

How will Hadoop and Big Data help you with your career?

The following forecasts will help you to understand Big Data growth:
  • Hadoop developers have an average salary of INR 11,74,000.
  • Organizations are interested in large data and use Hadoop to store and analyze them. There is therefore also a rapid increase in demand for jobs in Big Data and Hadoop. Now is the right place for Big Data Hadoop online training if you have an interest in a career in this field.

How long does it take to learn Big data and Hadoop?

You will take a couple of days to master the subject if you already fulfill the requirements for Hadoop. It can take 2 to 3 months to learn Hadoop, however, if you learn from scratch. In these cases, Big Data Hadoop Training is strongly recommended.

What can I learn Big data and Hadoop course?

Some key Big data topics here you need to know:
  • Concepts OOPS
  • Basics such as data types, syntaxes, casting type, etc.
  • Generics and collections like all MapReduce programs
  • Management of exceptions
  • Looping and conditional statements.

What are the job responsibilities of Big Data and hadoop?

Job Description for Hadoop Developers:
  • Development and implementation of Hadoop.
  • Hive and Pig are used for pre-processing.
  • Creating, constructing, installing, configuring, and maintaining Hadoop.
  • Analyze large amounts of data to find new insights.
  • Create data monitoring web services that are scalable and high-performing.
Show More

Overview of Big data Training in Mumbai

As the demand for Big Data grows, organisations are already on the lookout for Hadoop specialists. Because we are the top Hadoop training institution in Mumbai, we teach you all you need to know to become an expert in Hadoop. The key features of our Hadoop training include comprehending Hadoop and Big Data, Hadoop Architecture and HDFS, and the function of Hadoop components, as well as integrating R and NoSQL with Hadoop. Both beginners and experts can benefit from our Hadoop courses in Mumbai. Your lessons will prepare you for a variety of Hadoop positions that pay anywhere from 4 lakhs to 16 lakhs per year in average salaries.


Additional Info

Big data is comprised of five vital components

By industry experts, big data is typically described by the 5 Vs, which should be addressed separately, but in relation to the other pieces.

Volume:- Prepare a plan for how and where the data will be stored, as well as the amount of data needed.

Variety:- Analyze all the sources of data that are involved within an ecosystem and learn how to incorporate those sources into the system.

Velocity:- Today's businesses rely heavily on speed. The big data picture should be developed in real-time by deploying the right technologies.

Veracity:- When you put garbage in, you get garbage out, so make your data accurate and up to date. Use a big data system to surface actionable business intelligence in an easy-to-understand manner using gathered environmental data.

Virtue:- all regulations for data privacy, privacy protection, and compliance need to be addressed as well when using big data.

What makes big data so important?

We live in a digital world where consumers expect immediate results. Modern cloud-based business world deals with digital sales transactions, marketing feedback, and refinements at a blistering pace. Data is produced and compiled at a rapid rate in all of these transactions. It is important to put this information to use immediately so that we can effectively target our audience for a 360 view, or else we will lose customers to competitors who do.

Selecting a tool:

This process can be simplified significantly with the help of big data integration tools. When choosing a big data tool, you should look for the following features:

Connectors are everywhere:- there are many systems and applications in the world. Your team will be able to save more time if your integration tool has multiple pre-built connectors.

Open-source:- Open-source architectures typically provide greater flexibility, whereas they minimize vendor lock-in; also, many big data technologies are open source, making them easy to implement.

Portable:- In the hybrid cloud era, it is essential that companies be able to build big data integrations once and run them anywhere:- on-premises, hybrid and in the cloud.

Ease of use:- The interface should offer a simple and intuitive way for you to visualize your big data pipelines while learning how to use the tool.

Transparent pricing:- you shouldn't be penalized for adding connectors or data volumes to your big data integration solution.

Cloud compatibility:- Integration tools should be able to run natively in any cloud environment, including multi cloud and hybrid clouds, as well as use serverless computing to minimize the cost of your big data processing and pay only for what you use.

Hadoop consists of four main modules:

The HDFS (Hadoop Distributed File System) is one of the distributed file systems that runs on standard or low-end hardware. Aside from high fault tolerance and native support for large datasets, HDFS provides better data throughput than traditional file systems.

Yet Another Resource Negotiator (YARN):- Monitors and manages the resources used by cluster nodes. Scheduling jobs and tasks is done by it.

MapReduce:- Programs can use such a framework to perform parallel computation on data. This task converts data inputs into datasets that can be analyzed as key-value pairs. Reduce tasks consume map output to aggregate output and produce the desired results.

Hadoop Common:- All modules can access the common Java libraries.

Hadoop consists of what key features?

Hadoop's top 8 features are:

1) Effective Cost Management System:- The Hadoop framework can be implemented with little or no specialized hardware, making it a cost-effective system. So it does not matter what hardware it is implemented on. Commodity hardware is technical terminology for these components.

2) Nodes in a large cluster:- It supports a large cluster of nodes. This means a Hadoop cluster can be made up of millions of nodes. The main advantage of this feature is that it offers a huge computing power and a huge storage system to the clients.

3) Parallel Processing:- It supports parallel processing of data. Therefore, the data can be processed simultaneously across all the nodes in the cluster. This saves a lot of time.

4) Distributed Data:- Distributing and splitting data across cluster nodes are the responsibilities of Hadoop. Additionally, data is replicated over the entire cluster.

5) Fault management using automatic failover:- The Hadoop network is designed to replace a machine within a cluster in case of failure. The failed machine's configuration settings and data are also replicated to the new machine. Admins do not need to worry about this feature once it is properly configured on a cluster.

6) Optimizing the locality of data:- When a program is executed in the traditional way, data is transferred from the data center into the machine where it is being executed. Imagine, for instance, that the data this program uses is housed in a datacenter in the United States but is required in Singapore. The data size required is approximately 1 PB. Such a large amount of data would require a lot of bandwidth and time to transfer from the USA to Singapore. Hadoop solves this problem by moving the small amount of code that it contains. The code is transferred from the Singapore data center to the USA data center. This code is then compiled and executed locally. By doing this, a lot of bandwidth and time can be saved. Hadoop's ability to store large amounts of data is one of its most important features.

7) Cluster of heterogeneous cells:- Heterogeneous clusters are supported. In addition to being one of the most important features of Hadoop, it is also a key feature. Clusters that are heterogeneous are clusters where nodes are from different vendors. There are many versions and flavours of the operating system available for each of these computers. Think about a cluster with four nodes, for example. First, there is an IBM computer running RHEL (Red Hat Enterprise Linux), the second is an Intel computer running UBUNTU Linux, third is an AMD computer running Fedora Linux, and last is an HP computer running CENTOS Linux.

8) Scalability:- Cluster management refers to the process of adding or removing nodes, as well as adding or removing hardware components. Cluster operation is not affected or brought down in any way by this procedure. You can also add or remove individual hardware components such as RAM and hard drives.

What is Hadoop and how does it work?

There are two main components of Hadoop: the Hadoop Distributed File System (HDFS) and the MapReduce framework. As a result, each chunk of data is stored separately on a node in the cluster. Let us suppose we have 4 terabytes of data, and a Hadoop cluster with four nodes. A HDFS partition would split the data into four parts each of 1TB. Consequently, storing data on the disk would take significantly less time. For one part of the data to be stored on the disk, the total time for it to be stored would equal one part of the data. Data will be stored on a variety of machines simultaneously due to this fact. In order to provide high availability, Hadoop can replicate each part of the data onto other machines present in the cluster. The number of copies it can replicate depends on the replication factor. By default, the replication factor is set to three. The default replication factor will result in three copies of each part of the data being stored on three separate machines. There would be two copies of data stored on the same rack at the same time in order to reduce latency and bandwidth. On a different rack would be stored the last copy. Let's say Rack 1 and Rack 2 are on one rack, and Rack 3 and Rack 4 are on the other rack. Therefore, node 1 and node 2 would store the first two copies of part one. Node 3 or Node 4 will store the third copy. The remaining parts of the data are stored in a similar manner. Hadoop networking takes care of the nodes in the cluster to enable communication in order to distribute data. In addition, the ability to process large amounts of data simultaneously reduces the processing time.

The top 10 Hadoop tools for big data:

A list of the top 10 big data analytics tools for Hadoop is listed below.

1. Apache Spark:- Developed for ease of analytics operations, Apache Spark is an open-source analytics engine. Cluster computing platform that is designed for general-purpose use and is made to be fast. The Spark platform is designed to enable batch processing, machine learning, streaming data processing, and interactive queries.

2. Map Reduce:- Based on the YARN framework, MapReduce is just like an algorithm or a data structure. When we are dealing with Big Data, serial processing isn't as useful as it used to be since MapReduce can perform the distributed processing in parallel on a Hadoop cluster.

3. Apache Hive:- Hadoop is a platform for data warehousing, while Data Warehousing is about storing data at a single location that comes from many sources. Data analysis on Hadoop is made easy with Hive, one of the best tools. With SQL knowledge, Apache Hive can be used efficiently. HQL and HIVEQL are the query languages of high.

4. Apache Impala:- Open-source database engine Apache Impala runs on Hadoop. Impala's processing speed is faster than that of Apache Hive, so it overcomes the speed issue. Using similar SQL syntax, an ODBC module, and a similar user interface to Apache Hive, Apache Impala can be used. For data analytics purposes, Apache Impala can be incorporated with Hadoop easily.

5. Apache Mahout:- Mahout is derived from the Hindi word Mahavat, which means elephant rider. Hadoop and Mahout work together, so Mahout is named Apache Mahout. Mostly Mahout is used to implement Machine Learning techniques on our Hadoop environment like classification, collaborative filtering, recommendation. The Machine algorithms can be implemented using Apache Mahout without having to integrate with Hadoop.

6. Apache Pig:- Yahoo originally developed this Pig to make programming easier. Because it is built on top of Hadoop, Apache Pig can handle a large amount of data. Using Apache Pig, larger datasets can be analyzed by transforming them into a dataflow representation. The Apache Pig project also allows enormous datasets to be processed with greater abstraction.

7. HBase:- The HBase database consists solely of non-relational, NoSQL distributed, and columnar databases. A HBase database contains a number of tables containing multiple rows of data for each table. Multiple column families will be present in these rows, and these column families will contain key-value pairs. The HBase platform is based on HDFS (Hadoop Distributed File System). Whenever we need to search small-sized data from more massive datasets, we use HBase.

8. Apache Sqoop:- A command-line tool developed by Apache, sqoop, is a command-line application. In order to utilize HDFS(Hadoop Distributed File System), Apache Sqoop is primarily used to import structured data from RDBMS (Relational database management systems) like MySQL, SQL Server and Oracle. Our HDFS data can also be exported to RDBMS using Sqoop.

9. Tableau:- TIBCO Tableau is a software program for data visualization and business intelligence. Besides providing a variety of interactive visualizations to illustrate the insights of the data, it can also translate queries into visualizations and import any range and size of data.

10. Apache Storm:- It is built in Java and Clojure programming languages and is a free and open source distributed computing platform. It is compatible with a wide range of programming languages. It is faster to use Apache Storm for stream processing. Nimbus, Zookeeper, and Supervisor are some of the daemons available in Apache Storm. In addition to real-time processing and online Machine Learning, Apache Storm can also be used for many other tasks. There are so many companies using Apache Storm, including Yahoo, Spotify, and Twitter.

Big data: 5 major advantages of Hadoop:

1. Scalable:- The Hadoop storage platform is capable of storing and distributing very large data sets through a network of inexpensive, parallel servers. Hadoop enables businesses to run applications over thousands of nodes that involve thousands of terabytes of data, unlike traditional relational database management systems (RDBMS) that can't handle large amounts of data.

2. Cost effective:- Businesses' exploding data sets can also be stored cost effectively with Hadoop. As a result, traditional relational database management systems are extremely expensive to scale to a degree that will allow them to handle such high volumes of data. Many companies in the past had to reduce costs by down-sampling data and classifying it according to certain assumptions about which data was important. We would delete raw data, as storing them would be excessively expensive. As a result of this approach, when the business priorities changed, the complete raw data set could not be accessed because storing it was too expensive. In contrast, Hadoop applies a scale-out architecture to store all the data of a company for use at a later time. Hadoop allows for computing and storage to be done for only a few hundred pounds per terabyte instead of tens of thousands of pounds.

3. Flexible:- In the context of businesses, Hadoop enables quick and easy access to new data sources and the use of a variety of types of data (structured and unstructured) to generate value from this data. With Hadoop, businesses can get insight into data sources such as social media, email conversations, and clickstreams. As well as being used for log processing, recommendation systems, data warehousing, market campaign analysis, and fraud detection, Hadoop can also be used for a variety of other uses.

4. Fast:- Data is stored in Hadoop's distributed filesystem by means of a uniform map of data, located wherever the cluster's nodes are. The tools for data processing are often located on the same servers where the data is kept, resulting in faster data storage and processing. Hadoop can process terabytes of unstructured data in minutes, and petabytes in hours, if you are dealing with large volumes of unstructured data.

5. Resilient to failure:- Using Hadoop has the advantage of being fault tolerant. In addition to data being sent to an individual node, every node in the cluster receives a copy so that in the event of a failure, another copy can be accessed. A distributed NoNameNode architecture is more reliable, and MapR goes beyond that by eliminating the NameNode. A single or multiple failure is protected by our architecture.

How does the Big Data Hadoop certification help in jobs?

The job market is competitive today as there are only a limited number of openings available. It is likely you will not receive the job you want if you don't possess any specialization. The use of Hadoop for big data processing across various industries will lead to a growing demand for Big Data Hadoop professionals. Certification proves to recruiters that you have the Big Data Hadoop skills they're looking for. Top employers receive hundreds of thousands of resumes for a handful of job openings every week, so a Hadoop certification can set you apart. The average salary of a Certified Hadoop Administrator is 123,000. You can advance your career with Big data Hadoop certifications.

Show More

Key Features

ACTE Mumbai offers Hadoop Training in more than 27+ branches with expert trainers. Here are the key features,
  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


Syllabus of Hadoop Course in Mumbai
Module 1: Introduction to Hadoop
  • High Availability
  • Scaling
  • Advantages and Challenges
Module 2: Introduction to Big Data
  • What is Big data
  • Big Data opportunities,Challenges
  • Characteristics of Big data
Module 3: Introduction to Hadoop
  • Hadoop Distributed File System
  • Comparing Hadoop & SQL
  • Industries using Hadoop
  • Data Locality
  • Hadoop Architecture
  • Map Reduce & HDFS
  • Using the Hadoop single node image (Clone)
Module 4: Hadoop Distributed File System (HDFS)
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read,File Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
  • How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
  • FSCK Utility. (Block report)
  • How to override default configuration at system level and Programming level
  • HDFS Federation
  • ZOOKEEPER Leader Election Algorithm
  • Exercise and small use case on HDFS
Module 5: Map Reduce
  • Map Reduce Functional Programming Basics
  • Map and Reduce Basics
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
  • Types of Schedulers and Counters
  • Comparisons between Old and New API at code and Architecture Level
  • Getting the data from RDBMS into HDFS using Custom data types
  • Distributed Cache and Hadoop Streaming (Python, Ruby and R)
  • YARN
  • Sequential Files and Map Files
  • Enabling Compression Codec’s
  • Map side Join with distributed Cache
  • Types of I/O Formats: Multiple outputs, NLINEinputformat
  • Handling small files using CombineFileInputFormat
Module 6: Map Reduce Programming – Java Programming
  • Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching inside a file in Hadoop
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion,Split API discussion
  • Custom Data type creation in Hadoop
Module 7: NOSQL
  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail
  • Columnar Databases in Detail (HBASE and CASSANDRA)
  • TTL, Bloom Filters and Compensation
<strongclass="streight-line-text"> Module 8: HBase
  • HBase Installation, Concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • Master & Region Servers
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
  • Catalog Tables
  • Block Cache and sharding
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys)
  • Java API’s and Rest Interface
  • Client Side Buffering and Process 1 million records using Client side Buffering
  • HBase Counters
  • Enabling Replication and HBase RAW Scans
  • HBase Filters
  • Bulk Loading and Co processors (Endpoints and Observers with programs)
  • Real world use case consisting of HDFS,MR and HBASE
Module 9: Hive
  • Hive Installation, Introduction and Architecture
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store, Hive QL
  • OLTP vs. OLAP
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • User Defined Functions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
  • Dynamic Partition
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Bucketing and Sorted Bucketing with Dynamic partition
  • RC File
  • Compression on hive tables and Migrating Hive tables
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE
  • Log Analysis on Hive
  • Access HBASE tables using Hive
  • Hands on Exercises
Module 10: Pig
  • Pig Installation
  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on read
  • Primitive data types and complex data types
  • Tuple schema, BAG Schema and MAP Schema
  • Loading and Storing
  • Filtering, Grouping and Joining
  • Debugging commands (Illustrate and Explain)
  • Validations,Type casting in PIG
  • Working with Functions
  • User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail
  • SPLITS and Multiquery execution
  • Error Handling, FLATTEN and ORDER BY
  • Parameter Substitution
  • Nested For Each
  • User Defined Functions, Dynamic Invokers and Macros
  • How to access HBASE using PIG, Load and Write JSON DATA using PIG
  • Piggy Bank
  • Hands on Exercises
Module 11: SQOOP
  • Sqoop Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises
Module 12: HCatalog
  • HCatalog Installation
  • Introduction to HCatalog
  • About Hcatalog with PIG,HIVE and MR
  • Hands on Exercises
Module 13: Flume
  • Flume Installation
  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
Module 14: More Ecosystems
  • HUE.(Hortonworks and Cloudera)
Module 15: Oozie
  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
  • Zoo Keeper
  • HBASE Integration with HIVE and PIG
  • Phoenix
  • Proof of concept (POC)
Module 16: SPARK
  • Spark Overview
  • Linking with Spark, Initializing Spark
  • Using the Shell
  • Resilient Distributed Datasets (RDDs)
  • Parallelized Collections
  • External Datasets
  • RDD Operations
  • Basics, Passing Functions to Spark
  • Working with Key-Value Pairs
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Removing Data
  • Shared Variables
  • Broadcast Variables
  • Accumulators
  • Deploying to a Cluster
  • Unit Testing
  • Migrating from pre-1.0 Versions of Spark
  • Where to Go from Here
Show More
Show Less
Need customized curriculum?

Hands-on Real Time Hadoop Projects

Project 1
Specialized Analytics Project

The process of data analysis uses analytical and logical reasoning to gain information from the data. The main purpose of data analysis is to find meaning in data.

Project 2
Streaming Analytics Project

Streaming Analytics helps provide security protection because it gives companies a fast way to rapidly connect different events to detect security threat patterns.

Project 3
Streaming ETL Solution

This assignment is about building and implementing Extract Transform Load tasks and pipelines. The environment contains utilities that take care of Source-Sink analytics.

Project 4
Text Mining Using Hadoop

Hadoop technologies can be deployed for summarizing product reviews and conducting sentiment analysis. The product ratings given by customers.

Our Best Hiring Placement Partners

ACTE Mumbai offers arrangement openings as extra to each understudy/proficient who finished our study hall or internet preparing. A portion of our understudies are working in these organizations recorded underneath.
  • We give Big Data and Hadoop Training Certificate and industry important undertaking based preparing with a total 100% assurance.
  • Placement Team Concepts like group discussion, show abilities, conveyance abilities, objective setting, using time effectively and collaboration, composing content, resume building are canvassed in the program.
  • We will in general guarantee that our understudies stay drew in and their general learning experience is adaptable, helpful, and useful.
  • We likewise furnish our understudies with habitually asked talk with inquiries so our applicants can plan well for the meetings. Additionally, we plan mock meetings for our learners.
  • Our instructional class has been created in view of these elements to ensure that you are agreeable, skillful, and certain about these circumstances.
  • We get one of a kind occupation posts from organizations like HP, Google, TCS, Syntel, Capgemini, Infosys, and others.

Get Certified By MapR Certified Hadoop Developer (MCHD) & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees. Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

Get Certified

About Adequate Hadoop Instructor

  • Our Big Data and Hadoop Training in Mumbai have trainers comprehend the significance of the attributes of extraordinary Training, distinguish the right preparing techniques, in view of the applicant's profiles and reason.
  • Trainers are ensured talented experts with more than 15+ years of involvement with their separate fields.
  • Mentor's involvement with preparing has empowered our wannabes to become Guaranteed Big Data and Hadoop Training.
  • Our Instructors are entirely learned in their particular fields of work and have the potential and abilities needed to convey their substance.
  • By offering significant knowledge into inquiries questions and leading meetings through reenacted interviews, our teacher's guide candidates in building an expert CV and boosting their certainty.
  • To guarantee our candidates absolute satisfaction, our mentors have made a top to bottom course that meets their work needs and norms.

Hadoop Course Reviews

Our ACTE Mumbai Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.



"I would like to recommend to the learners who wants to be an expert on Big Data just one place i.e.,ACTE institute at Anna nagar. After several research with several Training Institutes I ended up with ACTE. My Big Data Hadoop trainer was so helpful in replying, solving the issues and Explanations are clean, clear, easy to understand the concepts and it is one of the Best Training Institute for Hadoop Training"


Software Engineer

Hi ACTE, I had taken Hadoop training for three weeks and it was excellent teaching along with hand on. I would definitely recommend this training for anyone who does not know Hadoop also in Mumbai.


Software Engineer

The training here is very well structured and is very much peculiar with the current industry standards. Working on real-time projects & case studies will help us build hands-on experience which we can avail at this institute. Also, the faculty here helps to build knowledge of interview questions & conducts repetitive mock interviews which will help in building immense confidence. Overall it was a very good experience in availing training in Tambaram at the ACTE Institute. I strongly recommend this institute to others for excelling in their career profession.



I had an outstanding experience in learning Hadoop from ACTE Institute. The trainer here was very much focused on enhancing knowledge of both theoretical & as well as practical concepts among the students. They had also focused on mock interviews & test assignments which helped me towards boosting my confidence.


Software Engineer

The Hadoop Training by sundhar sir Velachery branch was great. The course was detailed and covered all the required knowledge essential for Big Data Hadoop. The time mentioned was strictly met and without missing any milestone.Should be recommended who is looking Hadoop training course ACTE institute in Chennai.

View More Reviews
Show Less

Hadoop Course FAQs

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    • Gives
    • For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Hadoop training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Hadoop Course At ACTE?

  • Hadoop Course in ACTE is designed & conducted by Hadoop experts with 10+ years of experience in the Hadoop domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Hadoop batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Hadoop. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at +91 93800 99996 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
Show More
Request for Class Room & Online Training Quotation

      Related Category Courses

      Big Data Analytics Courses In Chennai

      Live Instructor LED Online Training Learn from Certified Experts Hands-On Read more

      cognos training acte
      Cognos Training in Chennai

      Beginner & Advanced level Classes. Hands-On Learning in Cognos. Best Read more

      Informatica training acte
      Informatica Training in Chennai

      Beginner & Advanced level Classes. Hands-On Learning in Informatica. Best Read more

      pentaho training acte
      Pentaho Training in Chennai

      Beginner & Advanced level Classes. Hands-On Learning in Pentaho. Best Read more

      obiee training acte
      OBIEE Training in Chennai

      Beginner & Advanced level Classes. Hands-On Learning in OBIEE. Best Read more

      web designing training acte
      Web Designing Training in Chennai

      Live Instructor LED Online Training Learn from Certified Experts Beginner Read more

      python training acte
      Python Training in Chennai

      Live Instructor LED Online Training Learn from Certified Experts Beginner Read more