Full Course (Batch) INR 16,199 40 Hrs Enroll Now

About Course

A wide variety of companies and organizations are using Apache Hadoop for both research and production environment.
Due to high demand of Big Data professionals in industry, Apache Hadoop is becoming popular and a must-know skill for the below job-roles
Project Managers
Software Architects
Data Management Professionals
Business Intelligence Professionals
Analytics Professionals
This course is designed to provide the training for Apache Hadoop which is a framework that allows distributed processing of large data sets across clusters of computers using simple programming models.
The advantage with Apache Hadoop is that it can scale up from single servers to thousands of machines, each offering local computation and storage to deliver high-performance without relying upon hardware.
Now a days, in the market there is a certification CCAH (Cloudera Certified Administrator for Apache Hadoop) backed by Cloudera. The CCAH validates the ability of an individual to configure, deploy, maintain, and secure an Apache Hadoop cluster.
This course will help you in understanding the Apache Hadoop system library from very beginning to advance so that you can crack your CCAH certification and able to configure, deploy, maintain, and secure an Apache Hadoop cluster.
After completing this course an individual can be able to work independently on the below modules of Apache Hadoop –
Hadoop Common Utilities
Hadoop Distributed File System (HDFS)
Hadoop YARN Framework.
Hadoop MapReduce.
Perform Data Analytics using Pig and Hive Hadoop Components
Mastering in Hadoop using concepts including Hbase, Zookeeper, and Sqoop.
There is no formal prerequisites for this course. Anyone can attend this course at anytime. Knowledge of java programming concept can add an advantage for you.


Big Data and Hadoop Administrator


  • 1.1 Describe the function of HDFS daemons
  • 1.2 Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing
  • 1.3 Identify current features of computing systems that motivate a system like Apache Hadoop
  • 1.4 Classify major goals of HDFS Design
  • 1.5 Given a scenario, identify appropriate use case for HDFS Federation
  • 1.6 Identify components and daemon of an HDFS HA-Quorum cluster
  • 1.7 Analyze the role of HDFS security (Kerberos)
  • 1.8 Determine the best data serialization choice for a given scenario
  • 1.9 Describe file read and write paths
  • 1.10 Identify the commands to manipulate files in the Hadoop File System Shell
  • 2.1 Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
  • 2.2 Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
  • 2.3 Understand basic design strategy for MapReduce v2 (MRv2)
  • 2.4 Determine how YARN handles resource allocations
  • 2.5 Identify the workflow of MapReduce job running on YARN
  • 2.6 Determine which files you must change and how in order to migrate a cluster from MapReduce
  • 2.7 version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN
  • 3.1 Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster
  • 3.2 Analyze the choices in selecting an OS
  • 3.3 Understand kernel tuning and disk swapping
  • 3.4 Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
  • 3.5 Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
  • 3.6 Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
  • 3.7 Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
  • 3.8 Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
  • 4.1 Given a scenario, identify how the cluster will handle disk and machine failures
  • 4.2 Analyze a logging configuration and logging configuration file format
  • 4.3 Understand the basics of Hadoop metrics and cluster health monitoring
  • 4.4 Identify the function and purpose of available tools for cluster monitoring
  • 4.5 Be able to install all the ecoystme components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Cloudera Manager, Sqoop, Hive, and Pig
  • 4.6 Identify the function and purpose of available tools for managing the Apache Hadoop file system
  • 5.1 Understand the overall design goals of each of Hadoop schedulers
  • 5.2 Given a scenario, determine how the FIFO Scheduler allocates cluster resources
  • 5.3 Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
  • 5.4 Given a scenario, determine how the Capacity Scheduler allocates cluster resources
  • 6.1 Understand the functions and features of Hadoop’s metric collection abilities
  • 6.2 Analyze the NameNode and JobTracker Web UIs
  • 6.3 Understand how to monitor cluster daemons
  • 6.4 Identify and monitor CPU usage on master nodes
  • 6.5 Describe how to monitor swap and memory allocation on all nodes
  • 6.6 Identify how to view and manage Hadoop’s log files
  • 6.7 Interpret a log file

Exam & Certification

Exam Code - (CCA-500)
Number of Questions: 60 questions
Time Limit: 90 minutes
Passing Score: 70%
Language: English, Japanese
CCA-500 certification is valid for two years.

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