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Hadoop Training in Trivandrum

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  • The course will teach you more than Hadoop, it will also teach you about Big Data. From installation to configuration to data handling, our course will make you an expert in big data.
  • As well as teaching you how they work together, this tutorial will also demonstrate how to use them to resolve real-world business problems!
  • A knowledge of UNIX and Java is the only prerequisite. The goal of this course is to provide you with the theoretical knowledge as well as the confidence needed to put this knowledge to use in your career.
  • The course prepares you for big data projects quickly. During this course, we will learn about HDFS, Map Reduce, Apache Pig, and Hive, as well as how to set up and configure Hadoop and EC2 instances.
  • The use of simple examples, applications, and explanations will allow students at all levels to understand all subjects.
  • Students can benefit from both theory and practical sessions. Students in our programs are prepared to obtain jobs in top companies after graduation.
  • You will learn Hadoop and its associated distributed systems in-depth, and you will be able to apply Hadoop to real-world problems. Furthermore, a valuable certificate of completion awaits 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.
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Course Objectives

It explains all core ideas of Hadoop in fun and simple means like HFS, Map Reduce, Apache Pig and Hive, etc. If you're obsessed with massive information and Hadoop then this can be an excellent course to start with. It's free, and you furthermore might get access to a multi-node Hadoop coaching cluster to follow on the course. You'd learn how to put in and build up a Hadoop Cluster from scratch. You'd become skillful in administering or managing the Hadoop Cluster. You'd additionally learn all the ideas associated with programming in Produce. You'd learn how to put in writing programs using produces.

You need to code to conduct numerical and applied mathematics analysis with Big Data Hadoop. A number of the languages you must invest time and cash in learning are Python, R, Java, and C++ among others. Finally, having the ability to assume sort of an engineer can help you to become a decent huge knowledge analyst.

You can indicate the certification test by taking data Analyst coaching, a knowledge analyst course designed for teaching students the way to access, manipulate, rework and analyze huge information sets within the Hadoop cluster using SQL and acquainted scripting languages. The minimum eligibility criteria for a postgraduate information Analytics course may be a degree with a minimum of five hundredth marks in combination or equivalent ideally in Science or applied science from a recognized university.

Hadoop provides a value-effective storage resolution for business. It facilitates businesses to simply access new information sources and faucet into differing types of data to supply prices from that data. It's an extremely accessible storage platform. Hadoop is over simply a quicker, cheaper info and analytics tool. Generating client offers supported their shopping habits. Rising client engagement and increasing client loyalty.

  • Big data analyst.
  • Big data coder.
  • Big data designer.
  • BI specialist.
  • Data engineer.
  • Data someone.
  • ETL developer.
  • Hadoop designer.

Big data helps corporations to plan valuable insights. Corporations use big data to refine their promoting campaigns and techniques. Corporations use it in machine learning projects to train machines, predictive modeling, and alternative advanced analytics applications. We will not equate big data to any specific information volume.

  • Analytical Skills.
  • Data visualization skills.
  • Familiarity with Business Domain and Big Data Tools.
  • Skills of Programming.
  • Problem-finding Skills.
  • SQL – Structured command language.
  • Skills of Data Mining.
  • Familiarity with Technologies.

Why should I choose the Big Data Hadoop Certification Training Course?

According to recent investigations, big data has developed across the years. The process, analyzing, sorting, and extracting data from datasets is straightforward. The technologies like Hadoop, Spark, Kafka, etc. Are serving to big data to boost structure performance. Information will reveal new revenue streams for businesses to extend their turnover, by serving to businesses understand their target client base. Big Data will facilitate optimize your practices and cut back waste and prices.

What are the tools needed for the Big Data Hadoop Certification Training Course?

Hadoop Distributed File System. The Hadoop Distributed filing system (HDFS) is meant to store giant knowledge sets faithfully, and to stream those knowledge sets at high information to user applications:
  • Hbase.
  • HIVE.
  • Sqoop.
  • ZooKeeper.
  • NOSQL.
  • Mahout.

What are the benefits of the Big Data Hadoop Certification Training Course?

  • Scalable. Hadoop may be an extremely accessible storage platform, as a result of it will store and distribute big data sets across many cheap servers that operate in parallel.
  • Cost-effective.
  • Flexible.
  • Fast.
  • Resilient to failure.

What are the consequences of the Big Data Hadoop Certification Training Course?

Big data can modification however even the smallest companies do business as data assortment and interpretation become a lot of accessible. New, innovative, and efficient technologies are forever rising and rising which creates it improbably straightforward for any organization to seamlessly implement big data solutions.

What is the role of Hadoop in Big Data?

In Hadoop large data files are distributed among several information, nodes suggest that cluster of artifact servers. It's not needed by the user to shop for or maintain any resources. Information keeps track of all hold on information that allows process and analysis easier. Therefore, Hadoop provides storage furthermore as a process tool.

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Overview of Hadoop Training in Trivandrum

With our Big Data & Hadoop Course, you will study the conceptual Hadoop framework and prepare for the Cloudera Hadoop Certificate Exam. Learn how various Hadoop components of the ecosystem fit in with the Big Data processing life cycle. Provides large-scale data Hadoop training for certification according to the industry-oriented Cloudera Certification test clearing. This is a master's program that offers you real expertise in Hadoop in the development, management, analysis, and testing of roles. At the end of the course and following the successful conclusion of the IBM project you will receive the IBM certification.

The course on Hadoop and Big Data will provide enhanced understanding and technology to become an efficient developer of Hadoop technology. In addition to studying, the virtual realization of applications for the live industry by using the key topic concepts. For easy accessibility and maintenance with basic programming modules, large data groupings can be preserved in simplified forms. Hadoop training is conducted with the greatest level of technology.

Additional Info

Intro of Big Data :

Big Data refers to data that scales exponentially with time and has a huge size. The publication is high volume, high velocity, and high variety. As large as the data sets are, traditional databases are insufficient to handle them. A continuous stream of data sets is generated with time and speed. For big data to be stored, there must be a lot of storage and processing tools that are fast. There are three types of big data: structured, semistructured, and unstructured.

Intro of Hadoop :

Hadoop is a software framework that is used to store and analyze large datasets and run applications on a cluster of commodity hardware.e. Master and slave tasks are executed in parallel on this system, so it has a lot of processing power. Besides providing high storage, it has high fault tolerance. Hadoop does not have any specific schema, and it can be deployed across physical servers in a distributed computing and storage configuration. Hadoop's processing capabilities are exceptional when it comes to handling complex data with high velocity. Several types of Hadoop exist, such as Apache, Cloudera, Horonworks, MapR, and IBM. Data transformations or preprocessing of data are not necessary. Among Hadoop's components are yarn, pig, hive, flume, HDFS, and mapreduce.

List Of Big Data Frameworks :

Our discussion will be focused on open-source massive data processing structures currently available. These are the only ones being used. But ideally, they ought to be viewed as a brief assessment of what is available and the potential of the used big data tool.

There are dozens of tools and technologies available at this time that deal with big data. They are all extraordinary at what they do, and there are many more. Anyhow, the ones we picked address :

  • Some of the most popular are Hadoop, Storm, Spark, and Hive.
  • MapReduce is one of the most useful programs.
  • Flink and Heron were the most encouraging.
  • Furthermore, the most underrated are Kudu and Samza.
  • 1. Hadoop :

    Batch processing is an open-source program that can be used for handling big data and distributed storage. A Hadoop framework is based on computer modules and clusters that have been planned with the expectation that hardware will eventually fail, and that these disappointments will be overcome by the framework.

    2. Storm :

    A big data framework designed around Apache Storm, whose applications are organized as coordinated acyclic graphs. In all programming languages, the storm can handle a wide range of streams effectively. Benchmarks indicate that it prepares more than 10,00,000 tuples each second for every node, is exceptionally adaptable, and ensures handling position.

    3. Spark :

    A very well-known and popular big data framework, its popularity continues to grow daily. Data workers can use Apache Spark's in-memory data preparation engine with a simple yet elegant application programming interface to perform structured querying, machine learning, and streaming jobs that require quick access to data iteratively.

    4. Hive :

    Facebook created Apache Hive to bring together the versatility of one of the most popular big data frameworks. It is an engine that converts structured query language-demands into chains of MapReduce challenges. In addition to Executor, Optimizer, and Parser, Apache Hive engine consists of several other segments. Big data hives can be coordinated with Hadoop for the examination of large quantities of data.

    5. MapReduce :

    An important part of Hadoop is MapReduce. Initially, Google introduced it in 2004 in order to prepare large amounts of raw data equally. As far as we might be concerned today, it was eventually called MapReduce data processing tools. As data pass through the engine, it is cyclically mapped, shuffled, and reduced.

    6. Presto :

    With Presto big data frameworks, users can run Interactive Analytic Queries against data wellsprings of all sizes up to Petabytes in size. Cassandra, Hive, proprietary data stores, and relational databases can be queried.

    7. Heron :

    List engines for big data include Apache Heron. This is another age substitute for Storm that Twitter created. This platform will be used to detect spam continuously, analyze trends, and perform ETL operations.

    8. Flink :

    The Apache Flink stream framework is among the top open-source frameworks for handling huge data streams. Data streaming applications that are accurate, highly available, and fast. Despite its fault-tolerance and statefulness, it can completely recover from failed operations. Provides excellent latency and throughput.

    9. Kudu :

    There is something energizing about Apache Kudu. This is an important big data framework targeted at improving some convoluted pipelines in the Hadoop environment. The structure of the query language is similar to that of the arrangement, and sequential and random queries can be written and read.

    10. Samza :

    A streaming data framework tool called Samza was being developed at LinkedIn for handling big data. Three layers make up the system: Streaming, Execution, and Processing. As well as pluggable architecture and streamable data, Samza incorporates horizontal scalability, operational ease, high performance, and high performance batch processing. Besides ADP, VMWare, Expedia, and Optimizely, Samza is also associated with a few brands.

Roles and Responsibilities of Big Data Developer :

Big Data Developers program Hadoop applications relevant to the Big Data domain. The following are their roles and responsibilities :

  • Comparing disparate data sets and loading the data.
  • Queries processed at high speeds
  • Standards and best practices should be proposed.
  • The responsibility of a Hadoop developer is to design, build, install, configure, and support Hadoop.
  • Secures and protects data.
  • HBase is managed and deployed by Big Data developers.
  • In his role, he analyzes data stored in a large number of servers and uncovers insights.
  • Big Data Developers are responsible for the development and implementation of Hadoop.
  • Creating high-performance, scalable web services to track data is a responsibility of his.
  • Developing detailed designs from complex technical and functional requirements is the role of a Big Data developer.
  • Various processes and products are changed and improved by him.

  • Skills required to become a Big data developer :

    • A working knowledge of Hadoop-based technologies or Big Data frameworks.
    • A working knowledge of Real-time processing frameworks (Apache Spark).
    • A familiarity with SQL-based technologies.
    • Experience with NoSQL based technologies like MongoDB, Cassandra, HBase.
    • Knowing one of the following programming languages (Java, Python, R).
    • Visualization tools such as Tableau, QlikView, and QlikSense are familiar.
    • Knowledge of different Data Mining tools such as Rapidminer, KNIME, etc.
    • A working knowledge of machine learning algorithms.
    • Quantitative and statistical analysis knowledge.
    • With FreeHand for Linux, Unix, Solaris or Windows.
    • Problem-solving skills and the ability to think creatively are essential.
    • Business knowledge is required.

    Hadoop Developer Job Responsibilities :

      Hadoop Developers have a variety of responsibilities, depending on the sector they work in. As mentioned in the job description of a Hadoop developer, the following responsibilities are generally involved:

    • Develop and document Hadoop applications, as well as design and develop Hadoop applications
    • Ensure that Hadoop is installed, configured, and maintained
    • Build new Hadoop clusters with MapReduce coding; provide assistance with MapReduce coding
    • Developing detailed designs based on hard and complex techniques and functional requirements
    • For the creation of web applications for data querying and tracking data more quickly at increased speeds
    • To propose standards and best practices; handover to the operations department
    • Prototypes of software are tested and then transferred to the operations team
    • Pig and Hive are used for pre-processing of data
    • Maintaining the security and privacy of data
    • HBase deployment and management
    • Analyzing and extracting insights from large data sets

    Hadoop Developer Skills :

    In order to hire the right candidate for the job, hiring managers look for a few particular skills. Hadoop developer skills are generally outlined in the following job description. Applicants who meet the criteria for the Hadoop developer job should satisfy all or part of these skills and be a fit to be considered for the position. The following skills are required for a job as a Hadoop developer :

    • An understanding of Hadoop ecosystem and its components is a must!
    • Write high-performance, reliable, and manageable code
    • A deep understanding of Hadoop's HDFS, Hive, Pig, Flume, and Sqoop.
    • Experience working with HQL
    • Extensive experience with Pig Latin and MapReduce
    • Hadoop concepts should be well-understood.
    • An analysis and problem-solving framework for the Big Data domain; implementation of these skills
    • Knowing how to use tools like Flume and Sqoop for loading data
    • Having a good understanding of database structure, theory, and principles

    Career Path :

    In the world of IT, Big Data Hadoop offers a tremendous opportunity to grow and gain knowledge. The following groups of IT professionals are continuously benefiting from moving into the Big Data domain :

    • Developers and Architects
    • BI /ETL/DW professionals
    • Senior IT Professionals
    • Testing professionals
    • Mainframe professionals
    • Freshers
    • The following benefits can be obtained through certification :

      • This increases your chances of landing highly coveted roles.
      • You become eligible for a variety of fields such as e-commerce, retail, healthcare, finance, entertainment, etc.
      • Less time, money, and effort is required for this than becoming an employee or getting a college degree.
      • Self-motivated and passionate about the subject.
      • Ensures your credibility as a subject matter expert.
      • Increased salary potential is a result.
      • Keep up with the latest trends in the industry with this tool.

      How does the Big Data Hadoop certification help in jobs?

      • In today's job market, there is intense competition for a limited number of jobs. In the event that you do not possess any specialization, chances are you won't get the job you're seeking.

      • Large enterprises across various industries are using Hadoop to process big data, which will lead to an increase in the need for Big Data Hadoop professionals. Certification lets recruiters know that you possess the skills they are looking for in Big Data Hadoop. Several hundred thousand resumes are submitted to top employers every week for a handful of job openings, so having a Hadoop certification can help you stand out. Additionally, Certified Hadoop Administrators command an average salary of $123,000 per year. Big DATA Hadoop certifications can therefore help you to advance your career.

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Key Features

ACTE Trivandrum 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 Trivandrum
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
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Hands-on Real Time Hadoop Projects

Project 1
Song Recommendations project

The objective of recommender systems is to provide recommendations based on recorded information on the users' preferences.

Project 2
Real-time Traffic Analysis project

Traffic analysis is the process of intercepting and examining messages in order to deduce information from patterns in communication, which can be performed.

Project 3
Analysis of Twitter Sentiment Using Spark Streaming project

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral.

Project 4
Analysis of Crime Datasets project

The aim of this project is to make crime predictions using the features present in the dataset. The dataset is extracted from the official sites.

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About Satisfactory Hadoop Mentor

  • Our Big Data and Hadoop Training in Trivandrum have content planned with work part with abundant involved task to make you project prepared.
  • The training gives a brought stage up in Polished skill, character prepping for the sprouting abilities or more all the making a quality individual to empower them to focus on the shoulder with best the corporate world.
  • Experts from corporate areas share their bits of knowledge on the area along these lines increasing the value of understudies learning.
  • Instructors model joins Inclination, Specialized and behaviourial abilities improvement as a piece of standard scholastic movement from the start of the course.
  • Coaches gives Mechanical preparing offers the chance to applicants to have a direct encounter of the business and its operational techniques.
  • Trainers are certified professionals with 9+ years of experience in their respective domain as well as they are currently working with Top MNCs.

Hadoop Course Reviews

Our ACTE Trivandrum 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

I joined for the Hadoop training with ACTE and completed the course by gaining good knowledge about the solution & satisfied with the overall training & also the trainer was very co-operative in Trivandrum.


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.

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  • 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
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