Big Data Hadoop Course in Chennai

We are the best provider of Big Data Hadoop Training in Chennai, Velachery and OMR includes the extreme knowledge about Hadoop concepts, its architecture and applications along with big data which covers Hadoop Distributed File System, Hadoop Clusters, Hadoop MapReduce, and it’s Ecosystem according to the positive reviews across the internet. Likewise, Our Hadoop training in Chennai is well designed to gain knowledge with the complete training program from absolute scratch and reach up to expert level.

In this Big Data Hadoop Training, the candidates obtains the live practical sessions on Data Engineering using SQL, NoSQL, Hadoop ecosystem, including most extensively used elements like HDFS, Spark, Hive, Sqoop, Impala & Cloud Computing. We offer both Classroom Training and Online Training to fulfill the advanced Hadoop training requirements and also provide Hadoop Certification Guidance. Also, you can download the Hadoop course content PDF below which has been designed by the experts in the industry. For more details: 9600112302

About Hadoop Course

Hadoop is an open source, big data tool, and Java-based programming framework widely used for the large-scale storage and processing of Big Data in the distributed computing environment. So, as a Hadoop developer, there is no limit to the opportunities for you and always a wanted for Big data experts in this digital world. Let’s dive in to know more about Hadoop course,

First of all, Big Data is a large volume of data as the name mentions “Big Data”. It is complex to collect using traditional data processed systems since the most of the data generation is unstructured form so it’s hard to handle the critical environment, So Hadoop is the solution to this problem which is an open source software framework for storing data and running applications on clusters of commodity hardware.

Hadoop is developed by Apache that is used by the global community of users and contributors. It can measure single to thousands servers having large data sets with high-powered analysis for smart decision making. Joining the Big Data Hadoop course in Chennai with placement will help you to learn from scratch.

Hadoop provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The most of the big IT companies use Hadoop to manage the large distributed datasets which increases the opportunity for many Hadoop positions. Also, the few big companies which use Hadoop are Google, Yahoo, IBM and EBay, Amazon etc., If you are passionate about Hadoop Training surely you can start your career in one of the best MNC's.
Nowadays Data is everything in this World. Here is the short story of how the data is rapidly increasing. Due to the vast variety of Technologies, Mobile Apps and Huge Amount of Social Networks, the amount of generating data is rapidly increasing every minute. Here is a small statistic.
  • The big data analytics market is set to reach $ 103 billion by 2023.
  • In 2020, 1.7 megabytes data is generated by everyone in just a second.
  • 2.5 quintillion bytes of data each day is generated by internet users.
  • 97.2% of organizations are investing in big data.
  • In 2012, only 0.5 % of data were analysed whereas 40 trillion gigabytes of data around 2020.
  • Just think about now and upcoming years!!
The graphical representation of Big Data application given below, Big Data Application

Key Features

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Hands on
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Certification
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HADOOP TRAINING COURSE CONTENT

Learning Outcomes of our Hadoop Course:

On successfully completing our 60 hours of Hadoop Training Program, You will be an expert with the below skills to match the industrial expectations,

  • Firstly, Strong knowledge in Hadoop Fundamental concepts.
  • Secondly, Deep Understanding of Hadoop Distributed File System(HDFS) and MapReduce concepts.
  • Morever, Installation and deployment of Apache Hadoop.
  • Most Importantly, Become an Expert in Map Reduce Programs and Implementation of HBase.
  • On the other words, Hands-on knowledge in Data loading techniques using sqoop and flume.
  • Likewise, Gain an depth knowledge of Big Data Framework using Hadoop and Apache Spark.
  • Further, Best practices in buliding, optimizing and debugging the Hadoop solutions.
  • In the conclusion, Over all understanding of Big Data Hadoop and be equipped to clear Big Data Hadoop Certification.

Hadoop Training Highlights

  • Learn Hadoop training from our Expert by working on hands-on real time projects.
  • Most importantly our Hadoop training in Chennai will start from complete scratch which includes spark with scala.
  • Further, Hands-on Practical assignments for each and every topic which makes you strong in technically.
  • On Successfully completion of this Hadoop training via online, classroom and corporate an individual. Also it will acquire the complete skillset required to be a professional Hadoop Developer.
  • In addition,Guidance for Hadoop Developer Certification.
  • Similarly, Special Combo Course (Hadoop and Spark) is available with Combo offer for interested candidates.
  • In addition, Latest Hadoop Job Openings will be shared with our trained Candidates.

Course Features

  • Duration60 hours
  • Skill levelAll level
  • Batch Strength15
  • AssessmentsYes
  • Mock InterviewsYes
  • Resume BuildingYes
  • PlacementsYes
  • Flexible TimingYes
  • Fee InstallmentsYes
  • LanguageTamil/English
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
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

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    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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    About Our Hadoop trainer

    The flood of data is increasing everywhere which implies the continuous need for big data experts in every organization to align the data with security. We ensure our expert tutors possess advanced subject knowledge and hands on practical experience to cope up with the industrial standard in effectively completing our Hadoop training in Chennai.

    • Our tutors are industrial big data experts from Hadoop framework who train in depth subject knowledge in a practical way that they have trained more than 1000+ candidates into certified experts.
    • Certified Big data Experts who are dedicated in completing the Hadoop course along with all the Big Data tools and practices.
    • Our Big Data Tutor having more than 8+years of working experience who are keen in training the aspirants with at most satisfaction
    • Our Mentors possess multiple domain knowledge like Machine learning, Python, Data Science along with industry related skills and practices to handle Hadoop curriculum.
    • Our trainers are dedicated professionals who are friendly and highly available to uplift the aspirants using assessments, real time projects, code review and doubt sessions.
    • Credo Systemz's trainers complete the Hadoop syllabus effectively along with resume building, mock interviews, career guidance to enable them to grab their dream job.

    Hadoop Combo Course

    First of all, Big Data Hadoop is one of the latest technology which has been listed down in top technologies survey result as well. Also, this open-source distributed processing framework is been developed by Apache Software Foundation which combines different open-source software utilities. According to the latest need and requirements, we have been also providing the Hadoop Combo Course which is listed down as follows,
    • Hadoop + Spark
    • Hadoop + Data Analytics
    • Hadoop + Data Science

    Hadoop Certification Details

    Credo Systemz Hadoop certification course up-skills the aspirants to complete Hadoop Certification in professional way to work on each and every component with practical and real-time scenarios based sessions you learn the Hadoop certification syllabus with the help of experts from top IT firms with complete hands on training. Similarly, our expert level certified trainers will help you to gain the required skill set to clear the examination easily.

    The Hadoop Developer certification details is given below,

    Exam CodeExam Name
    CCA175 CCA Spark and Hadoop Developer
    CCA159 CCA Data Analyst
    CCA131 CCA Administrator
    DE575 CCP Data Engineer

    Cloudera Certification Exam Information

    Cloudera has been Hadoop Certification for the last many years.

    • Cloudera Certified Professional - Data Scientist (CCA175)
    • Cloudera Certified Administrator for Hadoop (CCA159)
    • Cloudera Certified Hadoop Developer (CCA131)
    • Cloudera Certified Professional - Data Engineer (DE575)
    Here you go with more details about the Hadoop Certification.

    Cloudera Hadoop Certified Exams - (CCA175), (CCA159), (CCA131)

    Cloudera Hadoop Certified is a hands-on, practical exam using Cloudera technologies.

    Exam Details
    No of Questions8–12 performance-based hands-on tasks on a Cloudera Enterprise cluster.
    Time Limit120 minutes
    Passing Score70%
    LanguageEnglish

    Cloudera Certified Professional - Data Engineer (DE575)

    CCP Data Engineer exam is created to identify talented data professionals looking to stand out and into a drastic career growth.

    Exam Details
    No of Questions5–10 performance-based (hands-on) tasks on a pre-configured Cloudera Enterprise cluster.
    Time Limit240 minutes
    Passing Score70%
    LanguageEnglish

    Scope of Hadoop in Future

    First of all, Hadoop is a software framework created for Big Data management, storage, and processing and to run applications on clusters of commodity hardware. Big data Hadoop is the best choice for everyone who is interested to shine in the world of Big data. This is to say that multiple Career options are available for Hadoop developers in IT as well as other domains. Currently, there is a shortage in the Hadoop experts when compared to the demand since data continuously increases along with the increase in the investment of top organizations in the Big data field.

    The global Hadoop big data analytics market size grows from USD 12.8 billion in 2020 to USD 23.5 billion by 2025. So the Hadoop technology has lot of scope in future which increasing the job opportunities for Hadoop professionals to make an excellent career with better salary packages our Hadoop course in Chennai.

    Job Roles for Hadoop

    Hadoop framework is not limited to a single job role but makes you eligible to enter into multiple domains like Finance, Manufacturing, and telecommunication, so on. The followings are the few most wanted job roles with the Hadoop career

    • Hadoop Developer
    • Hadoop Administrator
    • Hadoop Architect
    • Hadoop Engineer
    • Hadoop Lead Developer
    • Data Scientist
    • Hadoop Tester

    Salary details for a Hadoop Developer?

    The Hadoop developer skills open the doors for a number of opportunities with no bar set for salary. If you want to get a high salary in the Hadoop developer job, your resume should contain the latest skill set which is possible with Our Hadoop training in Chennai.

    According to experts, the average Hadoop developer salary is Rs 559K per annum. The average salary for a Hadoop developer is 95% higher than the average salary for other open job positions. The range may vary from Rs 12 – Rs 18 per annum according to the experience of the professional.

    Hadoop Training with Placement in Chennai

    Credo Systemz's Hadoop training course is designed by experienced experts who are also working in the recruitment team of top MNC companies to cope up with the market needs. Our Hadoop certification consists of more number of practical sessions to gain real time experience which guide you towards placements.

    Our Placement team provides support in all ways till you get your dream job. The dedicated team starts helping the aspirants from scratch like professional resume building, soft skill training, interview calls and so on. Our Hadoop training Chennai is named for providing the best hands-on practical training with 100% guaranteed placement.

    Top Factors which makes us the Best Big Data Training Center in Chennai

    • Firstly, we are ranked as No.1 Best Hadoop Training institute in Chennai according to the positive reviews by our happy customers across the internet.
    • Effectively, offering Best Hadoop Certification Training in Chennai in an effective way on both weekday and weekends at flexible timings.
    • Most importantly our Hadoop Course in Chennai provides you the updated topics with all the latest concepts, tools, techniques and frameworks.
    • Moreover, you can attend the Free Demo Session with our Hadoop Experts to know more about the course - Book Now.
    • Our Hadoop training in Chennai, Velachery and OMR course provides you the latest updated Hadoop topics from scratch which is suitable for every passionate aspirant.
    • In addition to that, Our Hadoop training and certification at Credo Systemz is handled by certified Experts who are subject expertise, very dedicated professional to fulfil the industrial expectation.
    • In other words, Our Hadoop certifications Training will guide to you clear your certification exams along with the training.
    • On the other hand, job based Practical Oriented Hadoop training in Chennai which makes you strong in your technical skills.
    • Above all, referred as the Best Hadoop Certification Training in Chennai by our alumni team.
    • To emphasize, you can get real time practical oriented Hadoop training with 100% placement assistance using resume building, mock interview, doubt sessions and soft skill training.

    FAQ

    The necessary technical skills for learning Hadoop for beginners are
    • Basic concepts of Java and Linux.
    • Good Understanding of Database and SQL.
    • Good knowledge in mathematics and statistics.
    Your Passion is more important than anything else. No prerequisite is needed for Hadoop course using our Credo Systemz Hadoop training in Chennai because our Hadoop training program will start from scratch which will be helpful to learn from the basics easily.
    In our Hadoop training program, our trainers will cover all the important tools required for a Hadoop to handle the data as a professional.
    It includes the following,
    • Hadoop Distributed File System
    • MapReduce
    • Hbase
    • HIVE
    • PIG
    • Sqoop
    • Spark
    • OOZIE
    If you are looking for some specific tools just reach us via + 91 9884412301 / + 91 9600112302
    Our Hadoop Course Duration is 45 to 50 hours which covers all the modules of Hadoop training. In this duration, we will give you more case studies to practices and you will get various levels of Hadoop assessments. You have to work on a real time Hadoop application Project.
    As Big data is the future of the digital era, there will always be a need for professional Hadoop technologists with a good salary package. Hadoop Development will be the best choice for both beginners and experienced professionals for a better career growth.
    To know more check here the Hadoop Developer Career Path explained clearly with certification details.
    Our big data course in Chennai is to enrich the career of an individual into a professional in handling the real time project which is specially designed by Certified Experts You will develop and maintain the Hadoop application Projects individually and will be able to clear your Hadoop Certification after completing our Hadoop certification in Chennai. This will lead you into a professional Hadoop expert with your dream job with a successful career growth.
    YES!
    Our Interview support is part of our training program which ensures you to get your desired job using our dedicated placement team. After the course completion, we will conduct Three Mock Interviews to figure outs your Technical competence and your area of improvement. This will increase your Confident Level which is necessary for cracking the Interview.
    Sample Resume formats for All different Technologies.
    How can I book for a demo session? Easy! You can contact us anytime to book your Azure demo session and discuss with our consultant. Feel free to call us to get a clear idea. To book: Velachery + 91 9884412301 OMR + 91 9600112302 (Also available in WhatsApp)
    It’s a big Yes!
    We are providing 100% personal technical assistance for every trainee to get Cloudera Hadoop Certified Exams. With this professional Hadoop training program you will get the below benefits,
    • Practical project based training program.
    • 100% Placement assistance & Job oriented training approach which helps you to get top job opportunities.
    • Real time practical study with case studies and live project scenarios.
    • Mock interviews, Practical assignments, resume building guidance and more amazing features to the trainees.
    • Flexible batch timings -classes on both Weekdays and Weekends with your preferred timing.
    • Free demo classes and discussion with our experts before joining the course.
    • Lifetime support with any technical help.
    • Also, we assist our candidates to do the official certifications using our professional Hadoop experts.
    • Placement support ensure you get your dream job which includes Professional resume building, skill development, mock interview , interview calls.
    No hurries!! Credo Systemz allows you to select your preferred payment via Cash, Card, Cheque and UPI services.
    To know about our exciting offers, concessions and group discount. Call us now: + 91 9884412301 / + 91 9600112302
    You can attend your missed sessions with the upcoming Hadoop training program Our admin team arranges a compensation session within the batch or the next available batch. Also, we provide a recorded video of our live session for your reference.
    Feel free to enquire more. Mail us info@credosystemz.com or Call us now: + 91 9884412301 / + 91 9600112302

    Related Trainings

    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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