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  • Top 10 Big Data Interview Questions You Should Be Prepared For

Top 10 Big Data Interview Questions You Should Be Prepared For

When it comes to landing the job, Big Data engineers (such as you!) are expected to answer tricky interview questions that can leave them flustered and at a loss of words – right? Prior preparation of these top 10 Big Data interview questions will surely help in earning brownie points and set the ball rolling for a fruitful career. 
You may like to prepare for these questions in advance to have the correct answers up your sleeve at the interview table (also consider checking out this perfect parcel of information for data science degree).

Q # 10 Which are the essential Hadoop tools for effective working of Big Data?

Ans: Ambari, “Hive”, “HBase, HDFS (Hadoop Distributed File System), Sqoop, Pig, ZooKeeper, NoSQL, Lucene/SolrSee, Mahout, Avro, Oozie, Flume, GIS Tools, Clouds, and SQL on Hadoop are some of the many Hadoop tools that enhance the performance of Big Data.

For further reference: Click here

Q # 9 It’s true that HDFS is to be used for applications that have large data sets. Why is it not the correct tool to use when there are many small files?

Ans: In most cases, HDFS is not considered as an essential tool for handling bits and pieces of data spread across different small-sized files. The reason behind this is “Namenode” happens to be a very costly and high-performing system. The space allocated to “Namenode” should be used for essential metadata that’s generated for a single file only, instead of numerous small files. While handling large quantities of data attributed to a single file, “Namenode” occupies lesser space and therefore gives off optimized performance. With this in view, HDFS should be used for supporting large data files rather than multiple files with small data.

Suggested read - Fast Data or Big Data: What's right for you?

Q # 8 Which hardware configuration is most beneficial for Hadoop jobs?

Ans: It is best to use dual processors or core machines with 4 / 8 GB RAM and ECC memory for conducting Hadoop operations. Though ECC memory cannot be considered low-end, it is helpful for Hadoop users as it does not deliver any checksum errors. The hardware configuration for different Hadoop jobs would also depend on the process and workflow needs of specific projects and may have to be customized accordingly (also consider checking out this career guide for data science jobs).

Q # 7 What are the main distinctions between NAS and HDFS?

Ans

  • HDFS needs a cluster of machines for its operations, while NAS runs on just a single machine. Because of this, data redundancy becomes a common feature in HDFS. As the replication protocol is different in case of NAS, the probability of the occurrence of redundant data is much less.
  • Data is stored on dedicated hardware in NAS. On the other hand, the local drives of the machines in the cluster are used for saving data blocks in HDFS.
  • Unlike HDFS, Hadoop MapReduce has no role in the processing of NAS data. This is because computation is not moved to data in NAS jobs, and the resultant data files are stored without the same. 

For further reference: Click here

Q # 6 What do you mean by TaskInstance?

Ans: A TaskInstance refers to a specific Hadoop MapReduce work process that runs on any given slave node. Each task instance has its very own JVM process that is created by default for aiding its performance.

For further reference: Click here

Suggested read - Why Big Data is the new competitive advantage?

Q # 5 Why are counters useful in Hadoop?

Ans: Counters are an integral part of any Hadoop job as they are very useful for gathering relevant statistics. For instance, if a particular job consists of 150 node clusters with 150 mappers running at any given point of time, it becomes cumbersome and time-consuming to map and consolidate invalid records for the log entries. Here, counters can be used for keeping a final count of all such records and presenting a single output.

For further reference: Click here

Q # 4 How are file systems checked in HDFS?

Ans: The "fsck" command is used for conducting file system checks in Linux Hadoop and HDFS. It is helpful in blocking names and locations, as well as ascertaining the overall health of any given file system.

Q # 3 What do you mean by “speculative execution” in context to Hadoop?

Ans: In certain cases, where a specific node slows down the performance of any given task, the master node is capable of executing another task instance on a separate note redundantly. In such a scenario, the task that reaches its completion before the other is accepted, while the other is killed. This entire process is referred to as “speculative execution”.

You may also like - How To Bag Top Big Data Jobs

Q # 2 Pig Latin contains different relational operations; name them.

Ans: The important relational operations in Pig Latin are:

  • group
  • distinct
  • join
  • for each
  • order by
  • filters
  • limit

For further reference: Click here

Q # 1 Where does Hive store table data by default?

Ans: The default location for the storage of table data by Hive is:
hdfs://namenode/user/hive/warehouse

For further reference: Click here

These top 10 Big Data questions that you should be prepared for are just a few of the many that may come your way at a job interview. So, read and research as much as you can, and then, let your enhanced knowledge base do the talking for you.

All the best!

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