- Beginner & Advanced level Classes.
- Hands-On Learning in Hadoop.
- Best Practice for interview Preparation Techniques in Hadoop.
- Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
- Affordable Fees with Best curriculum Designed by Industrial Hadoop Expert.
- Delivered by 9+ years of Hadoop Certified Expert | 12402+ Students Trained & 350+ Recruiting Clients.
- Next Hadoop Batch to Begin this week – Enroll Your Name Now!
30 to 45 Days
Till get Placed
24 / 7 Support
Beginner | Expert
(Class 1Hr - 1:30Hrs) / Per Session
(Class 1Hr - 1:30Hrs) / Per Session
(Class 3hr - 3:30Hrs) / Per Session
(Class 4:30Hr - 5:00Hrs) / Per Session
Can't find a batch? Pick your own schedule
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
This is How ACTE Students Prepare for Better Jobs
About Hadoop Training Course in OMR
Hadoop, the technology is developed as part of an open source project within the Apache Software Foundation.Hadoop is not just one application, rather it is a platform with various integral components that enable distributed data storage and processing.Thus training in such would brighten yours career.
Top Job Offered Hadoop Tools Covered
Big Data, HDFS
Mahout, Spark MLLib
Is Hadoop good career choice?
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.
What is the scope of Hadoop?
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.
Is Hadoop enough to get a job?
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.
Will ACTE Help Me With Placements After My Hadoop Course Completion?
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.
Does Hadoop have an in-built Cluster Technology?
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.
What are the prerequisites for learning Hadoop?
- 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.
Can I learn Hadoop without Coding Experience?
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.
Will I Be Given Sufficient Practical Training 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.
Is it worth learning Hadoop?
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.
How long would it take to learn Hadoop?
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.
The Apache Hadoop framework is composed of the following modules
- Hadoop Common: contains libraries and utilities needed by other Hadoop modules
- Hadoop Distributed File System (HDFS): a distributed file-system that stores data on the commodity machines, providing very high aggregate bandwidth across the cluster
- Hadoop YARN: a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications
- Hadoop MapReduce: a programming model for large scale data processing
All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are common and thus should be automatically handled in software by the framework. Apache Hadoop's MapReduce and HDFS components originally derived respectively from Google's MapReduce and Google File System (GFS) papers.
Beyond HDFS, YARN and MapReduce, the entire Apache Hadoop "platform" is now commonly considered to consist of a number of related projects as well: Apache Pig, Apache Hive, Apache HBase, and others.
Use cases for Hadoop
Better real-time data-driven decisions
- Incorporate emerging data formats (streaming audio, video, social media sentiment and clickstream data) along with semi- and unstructured data not traditionally used in a data warehouse.
- More comprehensive data provides more accurate analytic decisions in support of new technologies such as artificial intelligence (AI) and the Internet of Things (IoT).
Improved data access and analysis
- Hadoop helps drive real-time, self-service access for your data scientist, line of business (LOB) owners and developers.
- Hadoop is helping to fuel the future of data science, an interdisciplinary field that combines machine learning, statistics, advanced analysis and programming.
Data offload and consolidation
- Optimize and streamline costs in your enterprise data warehouse by moving “cold” data not currently in use to a Hadoop-based distribution.
- consolidate data across the organization to increase accessibility, decrease cost and drive more accurate data-driven decisions.
- Hadoopshares many of the advantages of a traditional database system.
- Hadoop allows for the quick retrieval and searching of log data rather than using platform-specific query tools on each system.
- Hadoop scales well as data size grows by distributing search requests to cluster nodes to quickly find, process, and retrieve results.
- Hadoop is built primarily in Java and tools can be developed for real-time viewing and analysis of log data.
- Hadoop stores data as a structured set of flat files in Hadoop’s Distributed File System (HDFS) across the nodes in the Hadoop cluster. This allows Hadoop to support faster data insertion rates than traditional database systems.
- Hadoop is also fault tolerant making multiple copies of data across the cluster nodes so if a single node fails the data can still be retrieved from other nodes in the cluster.
- Hadoop provides distributed computing and distributed storage. It also enables the applications to work with millions of nodes and yottabytes of data
ACTE OMR 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
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 OMRModule 1: Introduction to Hadoop
- High Availability
- Advantages and Challenges
- What is Big data
- Big Data opportunities,Challenges
- Characteristics of Big data
- Hadoop Distributed File System
- Comparing Hadoop & SQL
- Industries using Hadoop
- Data Locality
- Hadoop Architecture
- Map Reduce & HDFS
- Using the Hadoop single node image (Clone)
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- HCatalog Installation
- Introduction to HCatalog
- About Hcatalog with PIG,HIVE and MR
- Hands on Exercises
- 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
- HUE.(Hortonworks and Cloudera)
- 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
- Proof of concept (POC)
- 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
- RDD Persistence
- Which Storage Level to Choose?
- Removing Data
- Shared Variables
- Broadcast Variables
- Deploying to a Cluster
- Unit Testing
- Migrating from pre-1.0 Versions of Spark
- Where to Go from Here
Hands-on Real Time Hadoop Projects
Customer churn analysis –Telecom Industry
The project involves tracking consumer complaints registered on various Platforms.
Determine dynamic pricing based on traffic congestion, Spark Streaming and Cassandra.
Our Top Hiring Partner for Placements
ACTE OMR 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
a physical version of your officially branded and security-marked Certificate.
About Experienced Hadoop Trainer
- Our Hadoop Training in OMR. 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 OMR from recognized IT organizations.
Hadoop Course 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"
I have Joined Hadoop course in ACTE. This Best Hadoop training in OMR. It is a good place for knowing and exploring more about Hadoop in depth. They teach you from the most basic topics no matter what is your stream. The faculties here are like the best teachers you can get for Hadoop training. Thanks to ACTE.
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.
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 OMR.
Hadoop Course FAQs
Looking for better Discount Price?
Does ACTE provide placement?
- 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
Is ACTE certification good?
- 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 and National Institute of Education (NIE) Singapore
Work On Live Projects?
- 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
Who are the Trainers?
What if I miss one (or) more class?
What are the modes of training offered for this Hadoop Course?
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