Hadoop Admin/Administration

Hadoop Admin/Administration

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Hadoop Administration Training in Noida

Hadoop Administration Training is provided by 10Daneces in Noida. We have highly experienced Faculty with more than 5 years of industry experience in top MNC's. Our Course is designed so as to fulfill all the industry requirements and make our students "Industry Ready". 10Daneces offers the best Classroom Training For Hadoop Admin. Our syllabus is designed by industry expert to provide our students a good learning experience.

The classroom course is scheduled on weekdays as well as on weekends. 10Daneces offers Fast Track Hadoop Administration Training in Noida. The weighty Subjects we cover under this Hadoop Administration course include Introduction, HDFS,Mapreduce, Advance Mapreduce Programming, Administration-Information required at Developer Level,HBase

10Daneces is unequivocally the best place where anybody keen to learn Big Data Hadoop can learn it excellently. Our classes provides deep understanding of the subject in such a way that anyone can be benefitted and become a specialist in the subject. Big Data Hadoop has been the vibrant force behind the development of the huge information construction.Hadoop has the ability to economically prepare a lot of information, paying no mind to its development.

The students gets the opportunity to gain each and every specialized detail and turn themselves into a professional. This course is extraordinarily designed in such a way that it finishes the whole syllabus in a brief span and saves time and money for individuals.

It is specially useful for individuals who are currently working in industries. The practical based Training modules arranged by 10Daneces helps a student to climb the ladder of success.

Hadoop Administration Training outfits you with the information and abilities to arrange, introduce, design, oversee, secure, screen, and investigate Hadoop Eco System parts and group. The Hadoop Administration Training in Noida is an ideal mix of intelligent addresses, hands-on practice, and occupation arranged educational modules. This Big Data Hadoop instructional class gives you a far reaching understanding on the fruitful usage of genuine Hadoop for industry ventures.

Benefits of Big Data/Hadoop Administration Training in Noida

  • Candidates with Hadoop Certification are preferred by recruiters
  • An edge over other professionals in the same field, in term of pay package.
  • The Certification helps you to outshine your career..
  • Helpful for People who are trying to change into Hadoop from different technical backgrounds.
  • Provide hands-on experience in dealing with Big Data.
  • Includes latest features of Hadoop.

Key Features of Hadoop V 2.0 & Big Data Administrator Training Training are:

  • Design POC (Proof of Concept): This process is used to ensure the feasibility of the client application.
  • Video Recording of every session will be provided to candidates.
  • Live Project Based Training.
  • Job-Oriented Course Curriculum.
  • Course Curriculum is approved by Hiring Professionals of our client.
  • Post Training Support will helps the associate to implement the knowledge on client Projects.
  • Certification Based Training are designed by Certified Professionals from the relevant industries focusing on the needs of the market & certification requirement.
  • Interview calls till placement.

Fundamental: Introduction to BIG Data

Introduction: Apache Hadoop

  • Why Hadoop?
  • Core Hadoop Components
  • Fundamental Concepts

Hadoop Installation and Initial Configuration

  • Deployment Types
  • Installing Hadoop
  • Specifying the Hadoop Configuration
  • Performing Initial HDFS Configuration
  • Performing Initial YARN and MapReduce Configuration
  • Hadoop Logging

Hadoop Security

  • Why Hadoop Security Is Important
  • Hadoop Security System Concepts
  • What Kerberos Is and How it Works
  • Securing a Hadoop Cluster with Kerberos


  • HDFS Features
  • Writing and Reading Files
  • NameNode Memory Considerations
  • Overview of HDFS Security
  • Using the NameNode Web UI
  • Using the Hadoop File Shell


Fundamentals: Introduction to Hadoop and its Ecosystem

Advanced Configuration

  • Flume: Basics | Flume’s high-level architecture
  • Flow in Flume | Flume: Features
  • Flume Agent Characteristics | Flume Design Goals: Reliability
  • Flume Design Goals: Scalability | Flume Design Goals: Manageability
  • Flume Design Goals: Extensibility | Flume: Usage Patterns

Cloudera Certified Developer for Hadoop

(CCDH) Exam Code: CCD-410

Cloudera Certified Developer for Apache Hadoop Exam:
  • Number of Questions: 50 - 55 live questions
  • Item Types: multiple-choice & short-answer questions
  • Exam time: 90 Mins.
  • Passing score: 70%
  • Price: $295 USD

Syllabus Cloudera Develpoer Certification Exam

Infrastructure Objectives 25%
  • Recognize and identify Apache Hadoop daemons and how they function both in data storage and processing.
  • Understand how Apache Hadoop exploits data locality.
  • Identify the role and use of both MapReduce v1 (MRv1) and MapReduce v2 (MRv2 / YARN) daemons.
  • Analyze the benefits and challenges of the HDFS architecture.
  • Analyze how HDFS implements file sizes, block sizes, and block abstraction.
  • Understand default replication values and storage requirements for replication.
  • Determine how HDFS stores, reads, and writes files.
  • Identify the role of Apache Hadoop Classes, Interfaces, and Methods.
  • Understand how Hadoop Streaming might apply to a job workflow
Data Management Objectives 30%
  • Import a database table into Hive using Sqoop.
  • Create a table using Hive (during Sqoop import).Successfully use key and value types to write functional MapReduce jobs.
  • Given a MapReduce job, determine the lifecycle of a Mapper and the lifecycle of a Reducer.
  • Analyze and determine the relationship of input keys to output keys in terms of both type and number, the sorting of keys, and the sorting of values.
  • Given sample input data, identify the number, type, and value of emitted keys and values from the Mappers as well as the emitted data from each Reducer and the number and contents of the output file(s).
  • Understand implementation and limitations and strategies for joining datasets in MapReduce.
  • Understand how partitioners and combiners function, and recognize appropriate use cases for each.
  • Recognize the processes and role of the the sort and shuffle process.
  • Understand common key and value types in the MapReduce framework and the interfaces they implement.
  • Use key and value types to write functional MapReduce jobs.
Job Mechanics Objectives 25%
  • Construct proper job configuration parameters and the commands used in job submission.
  • Analyze a MapReduce job and determine how input and output data paths are handled.
  • Given a sample job, analyze and determine the correct InputFormat and OutputFormat to select based on job requirements.
  • Analyze the order of operations in a MapReduce job.
  • Understand the role of the RecordReader, and of sequence files and compression.
  • Use the distributed cache to distribute data to MapReduce job tasks. Build and orchestrate a workflow with Oozie.
Querying Objectives 20%
  • Write a MapReduce job to implement a HiveQL statement.
  • Write a MapReduce job to query data stored in HDFS.

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