Apache Hadoop and Mapreduce Interview Questions and Answers
Apache Hadoop and Mapreduce Interview Questions and Answers (120+ FAQ)
Hadoop,Development,Software Development Tools
Lectures -128
Duration -1.5 hours
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Course Description
Apache Hadoop and Mapreduce Interview Questions has a collection of 120+ questions with answers asked in the interview for freshers and experienced (Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer).
This course is intended to help Apache Hadoop and Mapreduce Career Aspirants to prepare for the interview.
We are planning to add more questions in upcoming versions of this course.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.
Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster.
Goals
What will you learn in this course:
- By attending this course you will get to know frequently and most likely asked Programming, Scenario based, Fundamentals, and Performance Tuning based Question asked in Apache Hadoop and Mapreduce Interview along with the answer
- This will help Bigdata Career Aspirants to prepare for the interview.
- During your Scheduled Interview you do not have to spend time searching the Internet for Apache Hadoop and Mapreduce Interview questions.
- We have already compiled the most frequently asked and latest Apache Hadoop and Mapreduce Interview questions in this course.
Prerequisites
What are the prerequisites for this course?
- Apache Hadoop and MapReduce basic fundamental knowledge is required
Curriculum
Check out the detailed breakdown of what’s inside the course
Section 1
10 Lectures
- Introduction 01:55 01:55
- How to unzip .gz files in a new directory in hadoop? 05:48 05:48
- Scenario Based Question 3 03:45 03:45
- How does Hadoop Namenode failover process works? 04:21 04:21
- Scenario Based Question 5 04:15 04:15
- How can we initiate a manual failover when automatic failover is configured? 00:30 00:30
- When not use Hadoop? 02:31 02:31
- Is there a simple command for hadoop that can change the name of a file ? 01:29 01:29
- When To Use Hadoop? 02:32 02:32
- Scenario Based Question 10 00:51 00:51
Section 2
10 Lectures
Section 3
10 Lectures
Section 4
10 Lectures
Section 5
10 Lectures
Section 6
10 Lectures
Section 7
10 Lectures
Section 8
10 Lectures
Section 9
10 Lectures
Section 10
10 Lectures
Section 11
10 Lectures
Section 12
10 Lectures
Section 13
8 Lectures
Instructor Details
Bigdata Engineer
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