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Big Data and Hadoop Training in Anna Nagar

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Rated #1 Recognized as the No.1 Institute for Big Data and Hadoop Training in Anna Nagar

Take the first step towards a successful career in big data with Big Data and Hadoop Training in Anna Nagar. Learn how to process and analyze large volumes of data with Hadoop’s suite of tools.

This course is perfect for anyone looking to build a career in data analytics or big data management. Whether you’re a beginner aiming to enter the field or a professional seeking to enhance your expertise, Big Data program equips you with the knowledge and skills to excel in the growing data-driven industry.

  • Join 16,854+ learners supported by over 400 trusted companies.
  • Gain real-world experience with live projects and practical exercises.
  • Benefit from affordable, high-quality training with 100% placement assistance.
  • Unlock diverse career prospects in big data analytics and Hadoop management.
  • Enroll in Big Data and Hadoop Training in Anna Nagar to gain knowledge of Hadoop technologies.
  • Master the skills to process, manage, and analyze large datasets using Hadoop and related tools.
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  • Case Studies and Projects 8+

  • Hours of Training 45+

  • Placement Assurance 100%

  • Expert Support 24/7

  • Support & Access Lifetime

  • Certification Yes

  • Skill Level All

  • Language All

Learn From Experts, Practice On Projects & Get Placed in IT Company

  • 100% Guaranteed Placement Support for Freshers & Working Professionals
  • You will not only gain knowledge of Hadoop Certification and advanced concepts, but also gain exposure to Industry best practices
  • Experienced Trainers and Lab Facility
  • Hadoop Professional Certification Guidance Support with Exam Dumps
  • Practical oriented / Job oriented Training. Practice on Real Time project scenarios.
  • We have designed an in-depth course so meet job requirements and criteria
  • Resume & Interviews Preparation Support
  • 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.
  • START YOUR CAREER WITH HANDOOP CERTIFICATION COURSE THAT GETS YOU A JOB OF UPTO 5 TO 12 LACS IN JUST 60 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

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  • Non-IT to IT (Career Transition) 2371+
  • Diploma Candidates3001+
  • Non-Engineering Students (Arts & Science)3419+
  • Engineering Students3571+
  • CTC Greater than 5 LPA4542+
  • Academic Percentage Less than 60%5583+
  • Career Break / Gap Students2588+
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Upcoming Batches

24-Mar-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

26-Mar-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

22-Mar-2025
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

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

23-Mar-2025
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

    Hear it from our Graduate

    About Hadoop Training Course in Anna Nagar

    Hadoop systems can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing, analyzing and managing data than relational databases and data warehouses provide. ACTE Hadoop training are designed in such a way that will upgrade the skill and make you to stand out in crowd. ACTE Imparts Hadoop Class Room & Online Training Course Enroll Now!!!

    Top Job Offered Hadoop Tools Covered

    • Big Data, HDFS

      YARN, Spark

      MapReduce

    • PIG, HIVE

      HBase

      Mahout, Spark MLLib

    • Solar, Lucene

      Zookeeper

      Oozie

    Hadoop skills are in demand – this is an undeniable fact! Hence, there is an urgent need for IT professionals to keep themselves in trend with Hadoop and Big Data technologies. Apache Hadoop provides you with means to ramp up your career and gives you the following advantages: Accelerated career growth.

    Hadoop is the supermodel of Big Data. If you are a Fresher there is a huge scope if you are skilled in Hadoop. The need for analytics professionals and Big Data architects is also increasing . Today many people are looking to pursue their big data career by grabbing big data jobs as freshers.

    Even as a fresher, you can get a job in Hadoop domain. It is definitely not impossible for anyone to land a job in the Hadoop domain if they invest their mind in preparing and putting their best effort in learning and understanding the Hadoop concepts.

    We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for Hadoop. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

    A Hadoop Cluster uses Master-Slave architecture. It consist of a Single Master (NameNode) and a Cluster of Slaves (DataNodes) to store and process data. Hadoop is designed to run on a large number of machines that do not share any memory or disks. These DataNodes are configured as Cluster using Hadoop Configuration files. Hadoop uses a concept of replication to ensure that at least one copy of data is available in the cluster all the time. Because there are multiple copy of data, data stored on a server that goes offline or dies can be automatically replicated from a known good copy.

    • To learn Hadoop and build an excellent career in Hadoop, having basic knowledge of Linux and knowing the basic programming principles of Java is a must. Thus, to incredibly excel in the entrenched technology of Apache Hadoop, it is recommended that you at least learn Java basics.
    • Learning Hadoop is not an easy task but it becomes hassle-free if students know about the hurdles overpowering it. One of the most frequently asked questions by prospective Hadoopers is- “How much java is required for hadoop”? Hadoop is an open source software built on Java thus making it necessary for every Hadooper to be well-versed with at least java essentials for hadoop. Having knowledge of advanced Java concepts for hadoop is a plus but definitely not compulsory to learn hadoop. Your search for the question “How much Java is required for Hadoop?” ends here as this article explains elaborately on java essentials for Hadoop.

    Apache Hadoop is an open source platform built on two technologies Linux operating system and Java programming language. Java is used for storing, analysing and processing large data sets. ... Hadoop is Java-based, so it typically requires professionals to learn Java for Hadoop.

    Yes, you can learn Hadoop, without any basic programming knowledge . The only one thing matters is your dedication towards your work. If you really want to learn something, then you can easily learn. It also depends upon on which profile you want to start your work like there are various fields in Hadoop.

    Our course ware 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.

    Yes It is worth , Future will be bright. Learning Hadoop will definitely give you a basic understanding about working of other options as well. Moreover, several organizations are using Hadoop for their workload. So there are lot of opportunities for good developers in this domain. Indeed it is!

    No Learning Hadoop is not very difficult. Hadoop is a framework of java. Java is not a compulsory prerequisite for learning hadoop. ... Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware.

    Hadoop framework can be coded in any language, but still, Java is preferred. For Hadoop, the knowledge of Core Java is sufficient, and it will take approximately 5-9 months. Learning Linux operating system: - It is recommended to have a basic understanding and working of the Linux operating system.

    Top reasons to consider a career in Hadoop?

    Hadoop brings in better career opportunities in 2015.

    Learn Hadoop to pace up with the exponentially growing Big Data Market.

    Increased Number of Hadoop Jobs.

    Learn Hadoop to pace up with the increased adoption of Hadoop by Big data companies.


    Why to Focus towards Hadoop

    • Increasingly, the focus of Hadoop users and vendors alike is on cloud deployments of big data systems. In addition to Amazon EMR, as Elastic MapReduce is now called, organizations looking to use Hadoop in the cloud can turn to a variety of managed services, including Microsoft's Azure HDInsight, which is based on the Hortonworks platform, and Google Cloud Dataproc, which is built around the open source versions of Hadoop and Spark.
    • To better compete with those offerings, Cloudera -- which, despite its name, still got about 90% of its revenue from on-premises deployments as of September 2019 -- launched a new cloud-native platform that month. The Cloudera Data Platform technology combines elements of the separate Cloudera and Hortonworks distributions and includes support for multi-cloud environments.

    Components of Hadoop and how it works

    • The core components in the first iteration of Hadoop were MapReduce, HDFS and Hadoop Common, a set of shared utilities and libraries. As its name indicates, MapReduce uses map and reduce functions to split processing jobs into multiple tasks that run at the cluster nodes where data is stored and then to combine what the tasks produce into a coherent set of results.
    • MapReduce initially functioned as both Hadoop's processing engine and cluster resource manager, which tied HDFS directly to it and limited users to running MapReduce batch applications.
    • That changed in Hadoop 2.0, which became generally available in October 2013 when version 2.2.0 was released. It introduced Apache Hadoop YARN, a new cluster resource management and job scheduling technology that took over those functions from MapReduce.
    • YARN -- short for Yet Another Resource Negotiator, but typically referred to by the acronym alone -- ended the strict reliance on MapReduce and opened up Hadoop to other processing engines and various applications besides batch jobs. For example, Hadoop can now run applications on the Apache Spark, Apache Flink, Apache Kafkaand Apache Storm 
    • In Hadoop clusters, YARN sits between HDFS and the processing engines deployed by users.
    • The resource manager uses a combination of containers, application coordinators and node-level monitoring agents to dynamically allocate cluster resources to applications and oversee the execution of processing jobs in a decentralized process. YARN supports multiple job scheduling approaches, including a first-in-first-out queue and several methods that schedule jobs based on assigned cluster resources.
    • The Hadoop 2.0 series of releases also added high availability and federation features for HDFS, support for running Hadoop clusters on Microsoft Windows servers and other capabilities designed to expand the distributed processing framework's versatility for big data management and analytics.
    • Hadoop 3.0.0 was the next major version of Hadoop. Released by Apache in December 2017, it added a YARN Federation feature designed to enable YARN to support tens of thousands of nodes or more in a single cluster, up from a previous 10,000-node limit.
    • The new version also included support for GPUs and erasure coding, an alternative to data replication that requires significantly less storage space.
    • Subsequent 3.1.x and 3.2.x updates enabled Hadoop users to run YARN containers inside Dockerones and introduced a YARN service framework that functions as a container orchestration platform.
    • Two new Hadoop components were also added with those releases: a machine learning engine called Hadoop Submarine and the Hadoop Ozone object store, which is built on the Hadoop Distributed Data Store block storage layer and designed for use in on-premises systems.
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    Key Features

    ACTE Anna Nagar 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 .

     

    Curriculum

    Syllabus of Hadoop Course in Anna Nagar
    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
    • SPLITS
    • 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
    • INDEXES and VIEWS
    • MAPSIDE JOINS
    • 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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    Need customized curriculum?

    Hands-on Real Time Hadoop Projects

    Project 1
    Customer churn analysis –Telecom Industry

    The project involves tracking consumer complaints registered on various Platforms.

    Project 2
    UBER Projects

    Determine dynamic pricing based on traffic congestion, Spark Streaming and Cassandra.

    Our Top Hiring Partner for Placements

    ACTE Anna Nagar offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.

    • We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
    • We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
    • After completion of 70% Hadoop training course content, we will arrange the interview calls to students & prepare them to F2F interaction
    • Hadoop Trainers assist students in developing their resume matching the current industry needs
    • We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
    • We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

    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 Experienced Hadoop Trainer

    • Our Hadoop Training in Anna Nagar. Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
    • As all Trainers are Hadoop domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
    • All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
    • Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
    • Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Hadoop training to the students.
    • We have received various prestigious awards for Hadoop Training in Anna Nagar from recognized IT organizations.

    Hadoop Course FAQs

    Looking for better Discount Price?

    Call now: +91-7669 100 251 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
      ACTE Gives Certificate 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
    • 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 project experience, job support, and lifetime resources.
    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 76691 00251 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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          Job Opportunities in Big Data

          More than 35% of Data Professionals Prefer Big Data. Big Data Is Widely Recognized as the Most Popular and In-demand Data Technology in the Tech World.

          You can Work as a
          Big Data Engineer
          Big Data Consultant
          Big Data Developer
          Big Data Administrator
          Big Data Specialist
          Big Data Solutions Architect
          Big Data Project Manager
          Upcoming In-Demand Jobs
          Data Scientist

          Machine Learning Engineer
          Data Architect
          Data Analyst
          Data Governance Specialist
          Salary In Big Data

          Reach Our Placement Officer

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