Hadoop Training in Chennai

Big Data Hadoop Training in Chennai at Credo Systemz which is handled by experienced professional. Our Big Data certification in chennai is focused on enhancing the skills of an individually in handling the data sets by real time case studies. Most importantly, joining this Big data courses in Chennai to here you can learn the top trending platforms like Hadoop ,Big Data Analytics and Spark Training. By our Hadoop training in Chennai with placement, you will attain 100% placement assistance from our placement team which incorporates the personal skill development, resume building, and updating about the current ongoing Hadoop job opportunities in India.

Credo Systemz  offering  Hadoop certification in chennai program as classroom, online and corporate training, you can book a free demo session with our Big Data Experts to choose your own Hadoop certification program.

Big Data Overview

Big Data is a term used to describe a massive volume of both structured and unstructured data so that is difficult to process in traditional database and software approach. It challenges include data analysis, search, capture, sharing, transfer, storage, visualization, and information privacy. It can be analyzed for insights and that leads to making better decisions and strategies for an organization.Our Big Data Certification course helps you to learn the tools practically and get certified, in order to build a successful career path.
Big Data Hadoop
Big Data is one of the most promising fields and massive scope of starting a career, considering all the technologies available in the IT market today. It leads to more efficient operations, smarter business moves, higher profits and happier customers. Big Data market will grow Seven Times faster than the overall IT market and there is a shortage of 1500000 Big Data professionals by the end of 2018. So this is the right time to start your career in Big Data.
The overall, importance of Big Data is continuously increased in day by day. This year, early in January Big Data survey report saying almost 80% of an organization implementing and expanding the Big Data for their projects.
Following are the few new trends on Big Data @ 2020:
Internet of Things - In Internet of Things (IoT) deployments, Big data doing a great job to handle with a large amount of data.
DataOps: DataOps is defined as the concept of Agile and DevOps. It has been used in Big data life cycle to achieve the target.
Machine Learning and Artificial Intelligence - Machine learning is an important part of artificial intelligence. Machine Learning is used to analyze the data and create new ideas without being explicitly.
Hybrid Clouds - Hybrid Clouds offering the storage of data with private and public advantages. Data has been stored in secure with its own storage.
Big Data Certification Training will help you to become a Big Data expert. It will enhance your skills by providing you complete knowledge on Big data and have a need for hands-on experience for solving real-time industry-based Big Data projects. In Big Data and Hadoop course, you will be trained by our expert of following skills given below,

  • Deep Understanding of Hadoop Distributed File System(HDFS), YARN and Map Reduce Concepts
  • How to work with Hadoop resource and storage management
  • Learn the data loading approaches using Sqoop and Flume
  • Understand the Hive and Pig Differences
  • Perform data analytics and ETL Operations using Pig and Hive
  • Master in HBase Architectures and Mechanisms
  • Work on real-time Mail Notification in Oozie
  • Implementing Bucketing, Partitioning and Indexing in Hive
  • Learn the Apache Spark and its Ecosystems
  • How to create and work with RDD in Apache Spark
  • Work on real-time Big Data Analytics projects
Big data applications are used in multiple domains in multiple applications. These are,
  • Education
  • Banking
  • Healthcare
  • Government Sector
  • Manufacturing
  • Media
  • Cybersecurity
  • Transportation

Key Features

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Training from
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24 x 7
Expert Support

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Hands on
Practicals/ Projects

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Certification
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100% Placement
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Big Data Hadoop Training Modules & Real-time Project

Section 1: INTRODUCTION TO BIG DATA-HADOOP
  • Overview of Hadoop Ecosystem
  • Role of Hadoop in Big data– Overview of other Big Data Systems
  • Who is using Hadoop
  • Hadoop integrations into Exiting Software Products
  • Current Scenario in Hadoop Ecosystem
  • Installation
  • Configuration
  • Use Cases ofHadoop (HealthCare, Retail, Telecom)
Section 2: HDFS
  • Concepts
  • Architecture
  • Data Flow (File Read , File Write)
  • Fault Tolerance
  • Shell Commands
  • Data Flow Archives
  • Coherency -Data Integrity
  • Role of Secondary NameNode
Section 3: MAPREDUCE
  • Theory
  • Data Flow (Map – Shuffle - Reduce)
  • MapRed vs MapReduce APIs
  • Programming [Mapper, Reducer, Combiner, Partitioner]
  • Writables
  • InputFormat
  • Outputformat
  • Streaming API using python
  • Inherent Failure Handling using Speculative Execution
  • Magic of Shuffle Phase
  • FileFormats
  • Sequence Files
Section 4: HBASE
  • Introduction to NoSQL
  • CAP Theorem
  • Classification of NoSQL
  • Hbase and RDBMS
  • HBASE and HDFS
  • Architecture (Read Path, Write Path, Compactions, Splits)
  • Installation
  • Configuration
  • Role of Zookeeper
  • HBase Shell  Introduction to Filters
  • RowKeyDesign -What's New in HBase  Hands On
Section 5: HIVE
  • Architecture
  • Installation
  • Configuration
  • Hive vs RDBMS
  • Tables
  • DDL
  • DML
  • UDF
  • Partitioning
  • Bucketing
  • Hive functions
  • Date functions
  • String functions
  • Cast function Meta Store
  • Joins
  • Real-time HQL will be shared along with database migration project

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Section 6: PIG
  • Architecture
  • Installation
  • Hive vs Pig
  • Pig Latin Syntax
  • Data Types
  • Functions (Eval, Load/Store, String, DateTime)
  • Joins
  • UDFs- Performance
  • Troubleshooting
  • Commonly Used Functions
Section 7: SQOOP
  • Architecture , Installation, Commands(Import , Hive-Import, EVal, Hbase Import, Import All tables, Export)
  • Connectors to Existing DBs and DW
Real-time Practicals
  • SQOOP to import Real Time Weblogs from application to DB and try to export the same to MySQL
Section 8: KAFKA
  • Kafka introduction
  • Data streaming Introduction
  • Producer-consumer-topics
  • Brokers
  • Partitions
  • Unix Streaming via kafka
Real-time Practicals
    Kafka
  • Producer and Subscribers setup and publish a topic from Producer to subscriber
Section 9: OOZIE
  • Architecture
  • Installation
  • Workflow
  • Coordinator
  • Action (Mapreduce, Hive, Pig, Sqoop)
  • Introduction to Bundle
  • Mail Notifications
Section 10: HADOOP 2.0 and Spark
  • Limitations in Hadoop
  • 1.0 - HDFS Federation
  • High Availability in HDFS
  • HDFS Snapshots
  • Other Improvements in HDFS2
  • Introduction to YARN aka MR2
  • Limitations in MR1
  • Architecture of YARN
  • MapReduce Job Flow in YARN
  • Introduction to Stinger Initiative and Tez
  • BackWard Compatibility for Hadoop 1.X
  • Spark Fundamentals
  • RDD- Sample Scala Program- Spark Streaming
Real-time Practicals
  • Difference between SPARK1.x and SPARK2.x
  • PySpark program to create word count program in pyspark
Section 11: Big Data Use cases
  • Hadoop
  • HDFS architecture and usage
  • MapReduce Architecture and real time exercises
  • Hadoop Eco systems
  • Sqoop - mysql Db Migration
  • Hive. -- Deep drive
  • Pig - weblog parsing and ETL
  • Oozie - Workflow scheduling
  • Flume - weblogs ingestion
  • No SQL
  • HBase
  • Apache Kafka
  • Pentaho ETL tool integration & working with Hadoop eco system
  • Apache SPARK
  • Introduction and working with RDD.
  • Multinode Setup Guidance
  • Hadoop latest version Pros & cons discussion
  • Ends with Introduction of Data science.
Section 12: Real Time Project
  • Getting applications web logs
  • Getting user information from my sql via sqoop
  • Getting extracted data from Pig script
  • Creating Hive SQL Table for querying
  • Creating Reports from Hive QL

Click Stream Data Analytics Report Project

ClickStream Data

ClickStream data could be generated from any activity performed by the user over a web application. What could be the user activity over any website? For example, I am logging into Amazon, what are the activities I could perform? In a pattern, I may navigate through some pages; spend some time over certain pages and click on certain things. All these activities, including reaching that particular page or application, clicking, navigating from one page to another and spending time make a set of data. All these will be logged by a web application. This data is known as ClickStream Data. It has a high business value, specific to e-commerce applications and for those who want to understand their users’ behavior.

More formally, ClickStream can be defined as data about the links that a user clicked, including the point of time when each one of them were clicked. E-commerce businesses mine and analyse ClickStream data on their own websites. Most of the E-commerce applications have their built-in system, which mines all this information.


ClickStream Analytics

Using the ClickStream data adds a lot of value to businesses, through which they can bring many customers or visitors. It helps them understand whether the application is right, and the application experience of users is good or bad, based on the navigation patterns that people take. They can also predict which page you are most likely to visit next and can-do Ad Targeting as well. With this, they can understand the needs of users and come up with better recommendations. Several other things are possible using the ClickStream Data.


Project Scope

In this project candidates are given with sample click stream data which is taken from a web application in a text file along with problem statements.

  • Users information in MySQL database.
  • Click stream data in text file generated from Web application.

Each candidate has to come up with high level system architecture design based upon the Hadoop eco systems covered during the course. Each candidate has to table the High-level system architecture along with designed eco systems and pros and cons will be discussed with all the other candidates. Finally, will choose the best possible optimal system design approach for implementation.

Candidates are given instructions to create an oozie work flow with the respective Hadoop Eco systems finalized based on the discussion. Candidates has to submit the project for the given problem statement and this will be validated by the trainer individually before course completion.


ECO System involved in click stream analytics Project
HDFS, Sqoop, Pig, Hive, Oozie

BIG DATA COURSES

Top MNC Hadoop Interview Questions

  1. What is Fact Table and Dimension Table (When I said that I am aware of Dataware house concept)
  2. What type of data we should store in Fact table and dimension table?
  3. There is a string in a Hive column, how you will find the count of a character. For example, the string is “hdfstutorial”, then how to count number of ‘t’.
  4. There is a table in Hive, and the columns are student id, score and year. Find the top 3 students based on the score in each year.
  5. There is a table having 500 Million records. Now you want to copy the data of that table in some other table, what best approach you will choose.
  6. You have 10 tables, and there are certain join conditions you have to put and then the result needs to be updated in another table. How you will do it and what best practice you will follow
  7. Which all analytical functions you have used in Hive
  8. Why we use bucketing
  9. what is actually hapeening in bucketing and when we apply
  10. How bucketing is different from Partition and why we use it
  11. If you have a bucketed table then can you take those records to Sqoop directly
  1. What are the differences between Hadoop and Spark?
  2. What are the daemons required to run a Hadoop cluster?
  3. How will you restart a NameNode?
  4. Explain about the different schedulers available in Hadoop.
  5. List few Hadoop shell commands that are used to perform a copy operation.
  6. What is jps command used for?
  7. What are the important hardware considerations when deploying Hadoop in production environment?
  8. How many NameNodes can you run on a single Hadoop cluster?
  9. What happens when the NameNode on the Hadoop cluster goes down?
  10. What is the conf/hadoop-env.sh file and which variable in the file should be set for Hadoop to work?
  11. Apart from using the jps command is there any other way that you can check whether the NameNode is working or not.
  12. Which command is used to verify if the HDFS is corrupt or not?
  13. List some use cases of the Hadoop Ecosystem
  14. Which is the best operating system to run Hadoop?
  15. What are the network requirements to run Hadoop?
  16. What is the best practice to deploy a secondary NameNode?
  17. How often should the NameNode be reformatted?
  18. How can you add and remove nodes from the Hadoop cluster?
  19. Explain about the different configuration files and where are they located.
  20. What is the role of the namenode?
  1. What is serialization?
  2. How to remove the duplicate records from a hive table?
  3. How to find the number of delimiter from a file?
  4. Replace a certain word from a file using Unix?
  5. How to import a table without a primary key?
  6. What is cogroup in pig?
  7. How to write a UDF in Hive?
  8. How you can join two big tables in Hive?
  9. The difference between order by and sort by?
  1. What is rack awareness? And why is it necessary?
  2. What is the default block size and how is it defined?
  3. How do you get the report of hdfs file system? About disk availability and no.of active nodes?
  4. What is Hadoop balancer and why is it necessary?
  5. Difference between Cloudera and Ambari?
  6. What are the main actions performed by the Hadoop admin?
  7. What is Kerberos?
  8. What is the important list of hdfs commands?
  9. How to check the logs of a Hadoop job submitted in the cluster and how to terminate already running process?
  1. What Hadoop components will you use to design a Craiglist based architecture?
  2. Why cannot you use Java primitive data types in Hadoop MapReduce?
  3. Can HDFS blocks be broken?
  4. Does Hadoop replace data warehousing systems?
  5. How will you protect the data at rest?
  6. Propose a design to develop a system that can handle ingestion of both periodic data and real-time data.
  7. A folder contains 10000 files with each file having size greater than 3GB.The files contain users, their names and date. How will you get the count of all the unique users from 10000 files using Hadoop?
  8. File could be replicated to 0 Nodes, instead of 1. Have you ever come across this message? What does it mean?
  9. How do reducers communicate with each other?
  10. How can you backup file system metadata in Hadoop?
  11. What do you understand by a straggler in the context of MapReduce
  1. Why Hadoop? (Compare to RDBMS)
  2. What would happen if NameNode failed? How do you bring it up?
  3. What details are in the “fsimage” file?
  4. What is SecondaryNameNode?
  5. Explain the MapReduce processing framework? (start to end)
  6. What is Combiner? Where does it fit and give an example? Preferably from your project.
  7. What is Partitioner? Why do you need it and give an example? Preferably from your project.
  8. Oozie – What are the nodes?
  9. What are the actions in Action Node?
  10. Explain your Pig project?
  11. What log file loaders did you use in Pig?
  12. Hive Joining? What did you join?
  13. Explain Partitioning & Bucketing (based on your project)?
  14. Why do we need bucketing?
  15. Did you write any Hive UDFs?
  16. Filter – What did you filter out?
  17. HBase?
  18. Flume?
  19. Sqoop?
  20. Zookeeper?
  1. What is Hive variable
  2. What is Object inspector
  3. Please explain Consolidation in hive
  4. What are the differences between MapReduce and YARN
  5. Can you differentiate between Spark and MapReduce
  6. Explain RDD and data frames in spark
  7. Can you write the syntax for Sqoop import
  8. WHat do you know about Hive views
  9. Difference between Hive external table and Hive managed Table
  10. What are the differences between HBase and Hive
  11. What are Orderby, sortby, and clustered by
  12. What is Speculative execution
  13. Which all Alter column command in hive you have worked
  14. What is lazy evaluation in pig?
  15. What is dynamic partition and static partition in hive?
  16. What is the use of partitions and bucketing in hive?
  17. Explain the flow of MapReduce program?
  18. What is default partition in MapReduce and how can we override it?
  19. What is difference between key class and value class in MapReduce?
  20. What is the level of sub queries in hive?
  21. What is transformation and action in spark?
  1. What is heap error and how can you fix it?
  2. How many joins does MapReduce have and when will you use each type of join?
  3. What are sinks and sources in Apache Flume when working with Twitter data?
  4. How many JVMs run on a DataNode and what is their use?
  5. If you have configured Java version 8 for Hadoop and Java version 7 for Apache Spark, how will you set the environment variables in the basic configuration file?
  6. Differentiate between bash and basic profile.
  1. Garbage Collection in Java – How it works?
  2. Different Types of Comprassions in Hive?
  3. Job Properties in Oozie
  4. How do you ensure 3rparty Jar files are available in Data Nodes.
  5. How do you define and use UDF’s in Hive
  6. If we have 10GB and 10MB file, How do you load and process the 10 MB file in map-reduce
  7. What are Joins in Hive in Map-Reduce Paradigm
  8. Apart from Map-side and reduce side joins any other joins in map-reduce?
  9. What is Sort-merge-Bucketing?
  10. How do we test Hive in production?
  11. What is the difference between Hashmap and HashTable
  12. What is bucketing
  1. What are the differences between Hadoop and Spark?
  2.  What are the real-time industry applications of Hadoop?
  3. How is Hadoop different from other parallel computing systems?
  4. In what all modes Hadoop can be run?
  5. Explain the major difference between HDFS block and InputSplit.
  6. What is distributed cache? What are its benefits?
  7. Explain the difference between NameNode, Checkpoint NameNode, and Backup Node.
  8. What are the most common input formats in Hadoop?
  9. Define DataNode. How does NameNode tackle DataNode failures?
  10. What are the core methods of a Reducer?
  11. What is a SequenceFile in Hadoop?
  12. What is the role of a JobTracker in Hadoop?
  13. What is the use of RecordReader in Hadoop?
  14. What is Speculative Execution in Hadoop?
  15. How can you debug Hadoop code?
  1. How will you decide whether you need to use the Capacity Scheduler or the Fair Scheduler?
  2. What are the daemons required to run a Hadoop cluster?
  3. How will you restart a NameNode?
  4. Explain about the different schedulers available in Hadoop.
  5. List few Hadoop shell commands that are used to perform a copy operation.
  6. What is jps command used for?
  7. What are the important hardware considerations when deploying Hadoop in production environment?
  8. How many NameNodes can you run on a single Hadoop cluster?
  9. What happens when the NameNode on the Hadoop cluster goes down?
  10. What is the conf/hadoop-env.sh file and which variable in the file should be set for Hadoop to work
  11. Apart from using the jps command is there any other way that you can check whether the NameNode is working or not.
  12. Which command is used to verify if the HDFS is corrupt or not?
  13. List some use cases of the Hadoop Ecosystem
  14. I want to see all the jobs running in a Hadoop cluster. How can you do this?
  15. Is it possible to copy files across multiple clusters? If yes, how can you accomplish this?
  16. Which is the best operating system to run Hadoop?
  1. Explain Hadoop streaming?
  2. What is HDFS- Hadoop Distributed File System?
  3. What does hadoop-metrics.properties file do?
  4. How Hadoop’s CLASSPATH plays a vital role in starting or stopping in Hadoop daemons?
  5. What are the different commands used to startup and shutdown Hadoop daemons?
  6. What is configured in /etc/hosts and what is its role in setting Hadoop cluster?
  7. How is the splitting of file invoked in Hadoop framework?
  8. Is it possible to provide multiple input to Hadoop? If yes then how?
  9. Is it possible to have hadoop job output in multiple directories? If yes, how?
  10. Explain NameNode and DataNode in HDFS?
  11. Why is block size set to 128 MB in Hadoop HDFS?
  12. How data or file is written into HDFS?
  13. How data or file is read in HDFS?
  14. How is indexing done in HDFS?
  15. What is a Heartbeat in HDFS?
  16. Explain Hadoop Archives?
  1. Configure slots in Hadoop 2.0 and Hadoop 1.0.
  2. In case of high availability, if the connectivity between Standby and Active NameNode is lost. How will this impact the Hadoop cluster?
  3. What is the minimum number of ZooKeeper services required in Hadoop 2.0 and Hadoop 1.0?
  4. If the hardware quality of few machines in a Hadoop Cluster is very low. How will it affect the performance of the job and the overall performance of the cluster?
  5. How does a NameNode confirm that a particular node is dead?
  6. Explain the difference between blacklist node and dead node.
  7. How can you increase the NameNode heap memory?
  8. Configure capacity scheduler in Hadoop.
  9. After restarting the cluster, if the MapReduce jobs that were working earlier are failing now, what could have gone wrong while restarting?
  10. Explain the steps to add and remove a DataNode from the Hadoop cluster.
  11. In a large busy Hadoop cluster-how can you identify a long running job?
  12. When NameNode is down, what does the JobTracker do?
  13. When configuring Hadoop manually, which property file should be modified to configure slots?
  14. How will you add a new user to the cluster?
  15. What is the advantage of speculative execution? Under what situations, Speculative Execution might not be beneficial?
  1. What is Apache Hadoop?
  2. Why do we need Hadoop?
  3. What are the core components of Hadoop?
  4. What are the Features of Hadoop?
  5. Compare Hadoop and RDBMS?
  6. What are the modes in which Hadoop run?
  7. What are the features of Standalone (local) mode?
  8. What are the features of Pseudo mode?
  9. What are the features of Fully-Distributed mode?
  10. What are configuration files in Hadoop?
  11. What are the limitations of Hadoop?
  12. Compare Hadoop 2 and Hadoop 3?
  13. Explain Data Locality in Hadoop?
  14. What is Safemode in Hadoop?
  15. What is Safemode in Hadoop?
  16. What is a “Distributed Cache” in Apache Hadoop?
  17. How is security achieved in Hadoop?
  18. Why does one remove or add nodes in a Hadoop cluster frequently?
  19. What is throughput in Hadoop?
  20. How to restart NameNode or all the daemons in Hadoop?
  1. How will you initiate the installation process if you have to setup a Hadoop Cluster for the first time?
  2. How will you install a new component or add a service to an existing Hadoop cluster?
  3. If Hive Metastore service is down, then what will be its impact on the Hadoop cluster?
  4. How will you decide the cluster size when setting up a Hadoop cluster?
  5. How can you run Hadoop and real-time processes on the same cluster?
  6. If you get a connection refused exception - when logging onto a machine of the cluster, what could be the reason? How will you solve this issue?
  7. How can you identify and troubleshoot a long running job?
  8. How can you decide the heap memory limit for a NameNode and Hadoop Service?
  9. If the Hadoop services are running slow in a Hadoop cluster, what would be the root cause for it and how will you identify it?
  10. How many DataNodes can be run on a single Hadoop cluster?

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06
Dec
Hadoop Training – Online & Classroom
11:00 am - 1:00 pm
Chennai
14
Dec
Hadoop Training – Online & Classroom
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Big Data Hadoop Developer career opportunities

Big Data Hadoop market report shares there will be significant growth in the year of 2020. The global market share of Hadoop has been increased all over the world including Asia, America, Europe, the Middle East, this results in more number of company growth and increase in the need for Hadoop Developers.
Big data Hadoop career opportunities
As an individual having Hadoop knowledge is very much required, Hadoop has also been listed as one of the important skillsets to have in 2020 according to Forbes 2020. Graphical representation of Hadoop Job opportunities given below,

Big Data Developer Job Roles

There are various job roles are available in Big Data domain, these are
  • Business Analyst
  • Big Data Engineer
  • Data Analyst
  • Hadoop Developer
  • Hadoop Admin
  • Database developer
  • Hadoop Tester
  • Machine Learning Engineer
  • Data Scientist

Big Data Developer Salary in India

According to the Paycale.com, the average salary of Big Data developer is Rs.720160 per year. Salary estimation are given below for your reference,
Big data Hadoop Salary Details

FAQ

You can contact us via +91 9884412301 / 9600112302 or reach us via the contact form to book your seat in the upcoming batch for your Hadoop Big data training.
Sure you can book your free demo session and career counseling with our trainer and placement coordinators. To book please feel free to contact us via +91 9884412301 / 9600112302.
In order to meet the need of every individual, we used to offer different batch schedules like the classroom, online, corporate training and fast track sessions. You can book your batch according to your needs.
Yes, of course, Our Hadoop training will be handled by experts in the industry who have more than 15+ years of IT experience, This course will be handled with live practicals and real-time project scenarios to give you the live working environment as a Hadoop Developer. And we at Credo Systemz handling the Hadoop training program for the past 6+ years via classroom, online and corporate training programs.
Feel free to contact us at any time via +91 9884412301 / 9600112302 to discuss further details even you can use our contact us form to share your details with us.
Yes, You can. Credo Systemz offers to you for instalment payment via Cash, Cheque, Card,and UPI services
Yes, We are proud to say, Credo Systemz provides the Real-time with job-oriented training program. So you can roll your Python training without any hesitation.
Yes, Our Big Data training program is designed with real-time projects, case studies, and practicals. So you will surely work on live projects during your training period. Also, it is suitable for both beginners and experienced professionals which enhance yourself to give the confidence to become a Big data expert

Top Best Big Data Certifications in 2020

Here are the Top Best Big Data Certifications in 2020, Just take a glance.

  • Data Mining and Applications Graduate Certificate
  • MCSE: Business Intelligence
  • SAS Certifications
  • Hortonworks
  • Oracle Business Intelligence (OBI)
  • IBM Certified Solution Advisor: Big Data & Analytics V1
  • HP Vertica Big Data Accredited Solutions Expert (ASE)
  • Certified Analytics Professional (CAP)
  • EMC Data Scientist Associate (EMCDSA)
  • Cloudera Certified Professional Data Engineer
  • Cloudera Certified Professional: Data Scientist (CCP: DS)
  • Cloudera Certified Administrator for Apache Hadoop (CCAH)
  • Data Science Council of America (DASCA)

To know know more about the above certifications, ring us Velachery: +91-9884412301 | OMR: +91-9600112302

Big Data Training and Placement in Chennai

Big Data Training Chennai – Our training program is designed to help the students who have successfully completed our training in Chennai and also give further experience in real time projects. We Provides 100% Placement assistance with following guidance,

  • Frequent latest job updates.
  • Resume building.
  • Mock Interviews
  • Interview questions.
  • Certification guidance.

Big Data Corporate Training in Chennai

Big Data Corporate Training – Our Corporate training program will help your organization to train your employees in this field to an expert level. Our Real-time industrial experts will make sure to fulfill all your expectations.

Contact us to book your Big Data Corporate Training Program.

Big Data Training and Certification in Chennai

Our Big Data Hadoop Certification is one of the recognized certifications which we provide to the candidate who successfully completes the project, mock interview, and assessment at the end of the session. As an individual, this certification will help you to showcase yourself as a professional in this technology and it will enhance your skillset as well.

According our 2019 report, nearly 12500+ candidates have completed their Hadoop certification in our institute and working in top ITs in and around India.

Checkout: Complete Big Data Hadoop Certification – Latest updated(2020)

Top Factors which makes us the Best Hadoop Training Center in Chennai

  • Credo Systemz is ranked as the Best Hadoop training institute in Chennai with placement for both Velachery and OMR, according to the more number of positive reviews across the internet.
  • We are offering hadoop training in chennai velachery and hadoop training in chennai omr on both Weekdays and Weekends at flexible timing.
  • Most Importantly Big Data course in Chennai velachery and OMR is handled by hadoop Professional Level Certified Trainers.
  • In addition, we are providing the Online and Corporate Hadoop Training on tailor-made fees structure.
  • For the most part, Our Hadoop Course Syllabus suits for both Beginners and Experienced Professional to enhance their skills.
  • Our Hadoop Instructor has more than 12+ years of Industry experience. As a result, you can get updated and learn latest hadoop Topics.
  • During the Big Data Hadoop course in chennai, you will get fully hands-on experience in real-time projects which boost the confidence level in aspirants to face the real-time challenges successfully.
  • We will conduct hadoop Assessments and Mock Interview, so that we can evaluate candidate’s performance individually.
  • Also, we will guide you to complete Big Data certification in chennai which will help you to stand out in the market.
  • In addition, our Big Data Online course you can also attend our free Big Data workshops and discuss with our consultant to know about the topics, case studies and real-time Hadoop projects that is included in this training program.
  • Consequently you will receive Job alerts to your registered email and whatsapp from our placement team and also we are doing Big Data hadoop training and placement in various ways.

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Nearby Access Areas
Our Velachery and OMR branches are very nearby access to the below locations.
Medavakkam, Adyar, Tambaram, Adambakkam, OMR, Anna Salai, Velachery, Ambattur, Ekkattuthangal, Ashok Nagar, Poonamallee, Aminjikarai, Perambur, Anna Nagar, Kodambakkam, Besant Nagar, Purasaiwakkam, Chromepet, Teynampet, Choolaimedu, Madipakkam, Guindy, Navalur, Egmore, Triplicane, K.K. Nagar, Nandanam, Koyambedu, Valasaravakkam, Kilpauk, T.Nagar, Meenambakkam, Thiruvanmiyur, Nungambakkam, Thoraipakkam, Nanganallur, St.Thomas Mount, Mylapore, Pallikaranai, Pallavaram, Porur, Saidapet, Virugambakkam, Siruseri, Perungudi, Vadapalani, Villivakkam, West Mambalam, Sholinganallur.
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