Big Data Interview Questions

Big Data Interview Questions and Answers

We know that big data means a vast amount of data or we can say that a large amount of data is calculated in terabytes or even more. Big data helps us to create valuable information from the raw data. In a recent trend, almost all organizations use the big data concept for their marketing, or we can say that to refine their marketing policies. The profession provides the highest-paying jobs in the past years.

Top 15 Big Data Interview Questions and Answers

Below are the top questions and answers of Big Data. These questions are helpful while giving mock tests or interviews.

Q1. Can we explain 5 V in big data?

Answer:

Volume: It is nothing but the amount of data that we stored in warehouses and this data we need to process as per our requirement because this is raw data.
Velocity: The velocity at which real-time data is produced is basically introduced with velocity.
Variety: Variety means a different set of data or we can consider structured, unstructured, and semi-structured data which is collected from different sources. For the processing of this data, we need to use different appropriate algorithms.
Veracity: The quality of the analyzed data is what we mean when we talk about “data veracity,” which basically refers to how trustworthy the data are.
Value: Data that has not been transformed into something useful is useless and meaningless.

Q2. What is the relationship between Hadoop and Big Data?

Answer:

Big Data and we also talk about Hadoop. Hadoop is an open-source framework for saving, processing, and analyzing unorganized, complex data sets to gain knowledge and insight. Thus, this is the connection between Hadoop and Big Data.

Q3. What are the different types of components in Hadoop?

Answer:

Distributed File System: This is a key storage system of Hadoop and it is mainly used to store different datasets.
MapReduce: MapReduce is the layer of Hadoop that is in charge of processing data. It sends a request to HDFS for the processing of both structured and unstructured data. By dividing the data into separate tasks, it is in charge of the parallel processing of a large volume of data. The process consists of two phases: Reduce and map. In layman’s terms, Map is a stage in which data blocks are read and made available for processing by executors—computers, nodes, and containers.
YARN: This is nothing but the framework used in Hadoop for resource management and provides real-time streaming.

Q4. What are the advantages of HDFS over NFS?

Answer:

The main advantages of HDFS are that it creates more than replicas of files and which can help us to reduce the traffic or we can say the bottlenecks of singles. As well as it stores the replicas of files in different locations and fault tolerance is one of the key points.

Q.5 What is a conceptual data model?

Answer:

The conceptual and most abstract data models are conceptual ones. This model has some minor annotation, but the overall arrangement and controls for the data relationships are set.

Q6. What are the different tools useful for big data?

Answer:

NodeXL, KNIME, Tableau, and Solver as well as many more.

Q7. Can we explain FSCK?

Answer:

HDFS uses the command FSCK, which stands for File System Check. It determines whether a file is corrupt, has its replica, or contains missing blocks. A summary report detailing the file system’s overall health is produced by FSCK.

Q8. What is commodity hardware?

Answer:

The commodity required the minimum of hardware resources to run the Apache Hadoop framework is referred to as commodity hardware. “Commodity Hardware” refers to any hardware that meets the minimum requirements for Hadoop.

Q9. Why do we use the JSP command?

Answer:

It is used to test all the daemons of Hadoop such as NameNode, DataNode, etc.

Q10. What are the modes required to run Hadoop?

Answer:

Local Mode: By default, Hadoop does not have distributed mode so it can run on a single java machine without HDFC. Basically, this mode uses the local file system and this mode is helpful for debugging.
Distributed Mode: In this mode, every daemon executes on a different java machine so it is required custom configuration and here we can use HDFS for input and output.
Fully Distributed Mode: This is nothing but the production mode of Hadoop and here we can use slave and master structures for implementation. This model is fully distributed and scalable and it provides more security.

Q11. What is the input format used in Hadoop?

Answer:

Basically, there are three input formats used as follows.

Text: This is the default format used in Hadoop.
Key Value: By using this format we can easily read plain text.
Sequence file: By using this format we can read all files in the sequence.

Q12. What are the methods used for reducers?

Answer:

setup(): It is used to configure the different parameters for the reducer.
Reduce(): This is the very method used to reduce the operation of specific functions which means how to handle the task.
Cleanup(): By using this method we can clean all the temporary data or files which we no longer support and this activity performs after the reduce() method.

Q13. What is heterogeneity?

Answer:

Heterogeneity is related to Mapreduce, which basically is an application that allows us to access service and execute it over a heterogeneous network. While the execution of service considered all components such as hardware device, operating system, programming language, and network.

Q14. Why do we use –compress-code?

Answer:

By using this parameter we can get the output file of sqoop.

Q15. Can we explain data preparation?

Answer:

It is nothing but the cleaning of raw data which we need to process Prior to processing, this crucial step typically involves reformatting, enhancing, and consolidating data sets to enrich data.

Conclusion

From this article, we are able to understand the Big Data Interview Question. It provides the basic idea about the mid-level and higher-level concepts of ruby programming. Big Data Interview Question is a key point for every interview or we can say that every technology.