Big Data Interview Questions and Answers
Question - 61 : - How do you convert unstructured data to structured data?
Answer - 61 : -
An open-ended question and there are many ways to achieve this.
- Programming: Coding/ Programming is the most tried out method to transform unstructured data into a structured form. Programming is advantageous to accomplish because we get independence with it, which you can use to change the structure of the data in any form possible. Several programming languages, such as Python, Java, etc., can be used.
- Data/Business Tools: Many BI (Business Intelligence) tools support the drag and drop functionality for converting unstructured data into structured data. One cautious thing before using BI tools is that most of these tools are paid, and we have to be financially capable to support these tools. For people who lack both experience and skills needed for option 1, this is the way to go.
Question - 62 : - What is data preparation?
Answer - 62 : -
Data preparation is the method of cleansing and modifying raw data before processing and analyzing it. It is a crucial step before processing and usually requires reformatting data, making improvements to data, and consolidating data sets to enrich data.
Data preparation is an unending task for data specialists or business users. But, it is essential to convert data into context to get insights and then, can eliminate the biased results found due to poor data quality.
For instance, the data construction process typically includes standardizing data formats, enhancing source data, and/or eliminating outliers.
Question - 63 : - What is Sequencefileinputformat?
Answer - 63 : -
Hadoop uses a specific file format which is known as Sequence file. The sequence file stores data in a serialized key-value pair. Sequencefileinputformat is an input format to read sequence files.
Answer - 64 : -
This is due to the performance issue of NameNode. Usually, NameNode is allocated with huge space to store metadata for the large-scale file. The metadata is supposed to be a from a single file for optimum space utilization and cost benefit. In case of small size files, NameNode does not utilize the entire space which is a performance optimization issue.
Question - 65 : - Explain NameNode recovery process.
Answer - 65 : -
The NameNode recovery process involves the below-mentioned steps to make Hadoop cluster running:
- In the first step in the recovery process, file system metadata replica (FsImage) starts a new NameNode.
- The next step is to configure DataNodes and Clients. These DataNodes and Clients will then acknowledge new NameNode.
- During the final step, the new NameNode starts serving the client on the completion of last checkpoint FsImage loading and receiving block reports from the DataNodes.
Answer - 66 : -
NameNode – Port 50070
Task Tracker – Port 50060
Job Tracker – Port 50030
Question - 67 : - What is Distributed Cache in a MapReduce Framework
Answer - 67 : -
Distributed Cache is a feature of Hadoop MapReduce framework to cache files for applications. Hadoop framework makes cached files available for every map/reduce tasks running on the data nodes. Hence, the data files can access the cache file as a local file in the designated job.
Question - 68 : - What are the common input formats in Hadoop?
Answer - 68 : -
Below are the common input formats in Hadoop –
- Text Input Format – The default input format defined in Hadoop is the Text Input Format.
- Sequence File Input Format – To read files in a sequence, Sequence File Input Format is used.
- Key Value Input Format – The input format used for plain text files (files broken into lines) is the Key Value Input Format.
Answer - 69 : -
How to Approach: Unstructured data is very common in big data. The unstructured data should be transformed into structured data to ensure proper data analysis. You can start answering the question by briefly differentiating between the two. Once done, you can now discuss the methods you use to transform one form to another. You might also share the real-world situation where you did it. If you have recently been graduated, then you can share information related to your academic projects.
By answering this question correctly, you are signaling that you understand the types of data, both structured and unstructured, and also have the practical experience to work with these. If you give an answer to this question specifically, you will definitely be able to crack the big data interview.
Question - 70 : - How do you approach data preparation?
Answer - 70 : -
How to Approach: Data preparation is one of the crucial steps in big data projects. A big data interview may involve at least one question based on data preparation. When the interviewer asks you this question, he wants to know what steps or precautions you take during data preparation.
As you already know, data preparation is required to get necessary data which can then further be used for modeling purposes. You should convey this message to the interviewer. You should also emphasize the type of model you are going to use and reasons behind choosing that particular model. Last, but not the least, you should also discuss important data preparation terms such as transforming variables, outlier values, unstructured data, identifying gaps, and others.