Cognizant Hadoop Interview Questions

Here is the list of Hadoop Interview Questions which are recently asked in Cognizant company. These questions are included for both Freshers and Experienced professionals. Our Hadoop Training has Answered all the below Questions.

1. What Hadoop components will you use to design a Craiglist based architecture?

There are four major elements of Hadoop are HDFS, MapReduce, YARN, and Hadoop Common. Most of the tools or solutions are used to the supplement or support these major elements. All these tools work collectively to provide services such as absorption, analysis, storage and maintenance of data etc.

2. Why cannot you use Java primitive data types in Hadoop MapReduce?

The Java serialization requires the hash of the class to be prefixed before each instance of the object in the serialized format. Hence, to read the object, you do not need to specify the class name. This causes an overhead to read the object since each object can be an instance of the different classes.

3. Can HDFS blocks be broken?

Yes,HDFS blocks be brokento this is inputsplit. As HDFS does not know the content of the file. While storing the data into multiple blocks, last record of each block might be broken.

4. Does Hadoop replace data warehousing systems?

Hadoop will not replace a data warehouse because the data and its platform are two non-equivalent layers in Data warehouse architecture. However, there is more probability of Hadoop replacing an equivalent data platform such as a relational database management system.

5. How will you protect the data at rest?

Encryption at rest is designed to prevent the outsiders from accessing the unencrypted data by ensuring the sensitive data is encrypted when on disk. If an attacker obtains a hard drive with the encrypted data but not the encryption keys, the attacker must defeat the encryption to read the data.

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6. Propose a design to develop a system that can handle ingestion of both periodic data and real-time data.

Data can be streamed in the real-time or ingested in batches. When Big Data is ingested in real-time, then it is ingested immediately as soon as data arrives. When data is ingested in batches using the Data ingestion pipeline, data items are ingested in some chunks at a periodic time interval.

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?

Hadoop’s distributed processing framework can be used to count all unique users from 10,000 large files using approach like MapReduce, Hive, or Spark:

Apache Spark can handle large-scale data due to its distributed, in-memory processing capabilities. It allows users to read the files in parallel and process them to get the count of unique users.

Read the data: Spark can read the 10,000 files from HDFS.

Extract User Information: Parse the user information from each file.

Get Unique Users: Use Spark’s distinct() method to filter out duplicate users.

Count the Unique Users.

8. File could be replicated to 0 Nodes, instead of 1. Have you ever come across this message? What does it mean?

When a file is written to the HDFS, it is replicated to multiple core nodes. When you see this error, it means that the NameNode daemon does not have any available DataNode instances to write the data to in HDFS. In other words, block replication is not taking place.

9. How do reducers communicate with each other?

Reducers always run in the isolation and they can never communicate with each other as per the Hadoop MapReduce programming paradigm.

10. How can you backup file system metadata in Hadoop?

The backup file system metadata in Hadoop are:

Make sure the Standby NameNode checkpoints the namespace to fsimage_ once per hour.
Deploy monitoring on both NameNodes to confirm that checkpoints are triggering regularly.
Back up the most recent “ fsimage_ *” and “ fsimage_*.
Back up the VERSION file from the standby NameNode.

11. What do you understand by a straggler in the context of MapReduce

If a node crashes, MapReduce re-runs its tasks on a different machine. Equally importantly, if a node is available but is the performing poorly, a condition that we call a straggler, MapReduce runs a speculative copy of its task also called a “backup task”on another machine to finish the computation faster.

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Cognizant Hadoop Interview Questions