[Updated] Hadoop Admin interview Questions and Answers with Personal Experience | Big data | Hadoop

In this article, I shared my personal  experience on Hadoop admin interview questions and answers.Now a days most of the companies are asking these type of questions in the interview panel.

Hadoop Admin Interview Questions and Answers:

1.Difference between Cloudera, MapR distributions with examples?

Basically, Cloudera and mapR  are Big Data distributions for providing Hadoop components/services like HDFS, Hive, Hbase and Spark etc. Below link explain full details about distributions difference.

Cloudera vs MapR vs Hortonworks

2.How to handle backup mechanism in large data cluster?

In Hadoop eco-system there is no classic backup mechanism because HDFS uses block level replication for data protection.  Hadoop uses data replication using “distcp” command for replicate copies of data between cluster.

3.Explain how to resolve out of memory issues in job failures like Spark, Hive related jobs and explain each stage?

In this article explain Spark Performance tuning

4.What is Hadoop Heartbeat lost in the Hadoop cluster?

5.How to handle on Datanode goes down in the cluster? explain it?

6.What happens when Namenode is goes down suddenly?

7.Major difference between Hadoop HDFS and HBase in Big Data Environment.
In this article, will explain the major difference between HDFS and HBase.

Hadoop HDFS and HBase

8.Explain Hadoop components effectively using in your project?

As per your project related, you need to explain each and every Hadoop components.

9. What is Big Data? How to overcome the complexity data with Hadoop?

10. Difference between Impala and Hive in the Hadoop cluster?

Basically, Impala and Hive components works on top of Hadoop. Hive supports UDFS, more complexity queries, but Impala does not support. For more details about Impala vs Hive

11.How impala queries more faster than Hive queries?

As per my understating Hive query run on Map Reduce in backed but Impala doesn’t use MapReduce. It uses MPP(Massively parallel processing) so Impala querying is faster than Hive querying in Hadoop cluster.