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O’Reilly Media conceived the term ” Big Data” in 2005. Above all organizations are head-hunting for professionals in Big data. Besides this has become one of the most sought after high salaried jobs. Generally, there are enormous career opportunities in this segment throughout the world. The top 25 Big data interview questions & answers are stated below perfectly curated for assured success.
Q 1. What is Big Data?
In the first place, Big data refers to gigantic amounts of data especially datasets which are measurable in terabytes or petabytes.
For the most part, business enterprises gather the data they need in numerous ways eg. social media posts, internet cookies, transaction histories, email tracking, website interactions, and smartphones In the same manner online purchases, transaction forms, third-party trackers, and smartwatches. Internet of Things (IoT), server logs, user files, databases, and machinery sensors.
Q 2. Describe the steps for the deployment of the Big Data solution.
Demonstrably the following three steps are in practice for the deployment of Big Data solutions.
- Data Ingestion:- First extracting data from various sources is the primary step for deploying a Big Data solution. Usually, the data sourced may be from RDBMS like MySQL, CRM like SalesForce, and ERP systems like SAP along with documents, logfiles or social media feeds. Real-time streaming may be ingested by the data.
- Data Storage:- Consequently after the data is ingested, the next step is the storage of the extricated data. Ordinarily, the data may be stored in a NoSQL database or HDFS.
- Data Processing:-Generally Map Reduce, Spark, Pig etc. are processing frameworks which process the data. The final process in the big data solution is data processing.
Q 3. Name the five V’s in Big Data.
As a rule, the following Five V’s are considered in Big Data.
Volume: In the first place The enormous amount of data collected from multiple heterogeneous sources and stored in data warehouses emulates the volume. Inexplicably This quantum may be more than terabytes and petabytes.
Value: Secondly raw data is useless unless it is converted into something worthy. We may extract beneficial information.
Variety: In particular Big Data is comprised of structured, unstructured and semi-structured data compiled from various sources. On the other hand, this diversity of data requires specific processing techniques.
Velocity: Moreover Velocity refers to the rate at which data is being created in real-time in all industries.
Q 4. What is the function of Hadoop in Big Data Analytics?
Demonstrably Data analytics has become one of the criteria of business enterprises which are handling large amounts of unstructured, semi-structured and structured data. Likewise evaluating unstructured data is challenging and Hadoop participates with its potential for data collection, data storage and data processing. Besides this Hadoop is open source and runs on commodity hardware bringing cost-effective business solutions.
Q 5. Describe the command to format the Name Node.
Usually, the command to format the Name Node is:- $hdfsnamenode-format.
Q 6. What does the abbreviation fsck denote?
HDFS uses the command Fsck or “file system consistency check”
Q 7. What are the main dissimilarities between HDFS(Hadoop Distributed File System) and NAS( Network Attached Storage)?
In fact, the main dissimilarity between HDFS and NAS is that NAS runs on an individual machine whereas HDFS runs on a cluster of machines. However, the replication protocol is different in the case of NAS leading to lesser data redundancy. Nonetheless, data repetition is a common issue in HDFS.
Q 8. Hadoop and Big Data are interconnected by what?
Generally, they are analogous terms with the popularity of Big Data. Hadoop being a framework specializing in Big Data operation also became in demand.
Q 9. How is big data business helpful in increasing business earnings?
After all Big data assessment has become a boon to businesses, as it assists them to demarcate themselves from others and thereby boost their revenue. Generally, Big data analytics imparts customised guidance and suggestions. Usually, business houses take the help of it to launch new products, considering customer needs and preferences. Using this analytic tool, businesses are increasing their earnings. An increment in revenue in the range of 5-10% may be possible. As an example, some renowned firms like Bank Of America, Walmart, Twitter, Linkedin and Facebook are using Big data analytics to enhance their revenue exponentially.
Q 10.For Hadoop jobs, which hardware configuration is desirable?
As a matter of fact, for running Hadoop operations dual processors or core machines with ECC memory and configuration of 4/8 GB Ram are desirable. Nonetheless, the hardware configuration differs based on the process flow and project-specific workflow needing custom tailored.
Q 11. In the instance when two users try to access the same file in the HDFS, what happens eventually?
Usually, only the first user will receive the grant for file access as HDFS NameNode supports exclusive write only.
Q 12. In Hadoop, what do you understand by Rack awareness?
To illustrate Rack awareness is an algorithm which is applied to the name node, to decide the placing of blocks and their clones
Q 13. What is the diversity between “Input Split” and “HDFS Block”?
To illustrate Input Split is a logical division of data using a mapper for mapping operation. Moreover, the HDFS block splits the input data physically into blocks for data processing.
Q 14. Describe the core components of Hadoop
- Hadoop Map Reduce- Inexplicably Map Reduce has the responsibility for the parallel processing of a high volume of data by division of data into independent tasks. Map & Reduce. Thereby the” map is” the 1st stage of the process that identifies complex logic code and the “reduce” function is the second phase of processing that defines lightweight operations.
- Yarn is the processing framework in Hadoop.
Q 15. Describe a block in HDFS. What is its ideal size in Hadoop 1 and Hadoop 2?
Undoubtedly HDFS.Blocks are stored across Hadoop Cluster They are the smallest continuous data storage in a hard drive. Moreover, the ideal block size in Hadoop is 64 MB and the ideal block size in Hadoop 2 is 128 MB.
Q 16. Describe the features of Hadoop
Hadoop aids in the processing of Big data, which normally is very complex. Usually, some notable features of Hadoop are:-
- In particular Distributed Processing- ensuring quicker processing
2. Even so Open source
3. Fault Tolerance.
Q 17. How is HDFS better than NFS?
HDFS assists in creating multiple replicas of files, besides being fault tolerant. However, this decreases the blockage of many customers to want to access a single file. All the more since files have multiple images on various physical disks, reading performance scales better than NFS.
Q 18. What is data modelling?
Data modelling is a means to ensure a diagram by surveying the data in question and acquiring deep knowledge. Accordingly, the procedure of representing the data visually inspires the business and the technology professionals to understand the data and its usage.
Q 19. What are the diverse types of data models?
- Conceptual Data Model- In the first place, It is usually utilised in the development stage of a project. Logical data model- Enlarges on the basic framework set up in the conceptual model. This model is popular in data warehousing projects.
- Physical Data Model-The physical data model is the most panoramic and the penultimate step before database production. Thereby It usually portrays database management system-specific properties. and rules.
Q 20. Specify the common input formats in Hadoop.
Generally, the most common input formats in Hadoop are:-
- Firstly the “Text Input format”.by default settings
- Secondly to read Plain Text Files in Hadoop in “Key Value Input format”
- Thirdly for reading files in a sequence in Hadoop” Sequence File Input format”.
Q 21. What are the diverse Output formats in Hadoop?
The diverse Output formats in Hadoop are:-
- Hadoop’s default Output is “Text Output Format”
- Map files default in Hadoop” MapfileOutputFormat”.
- To write output in relational databases is “‘DBoutputformat “
Q 22. Provide details of the different procedures of Big Data processing.
Batch Processing of Big Data, Big Data Stream Processing, Real-time Big Data processing and lastly Map Reduce.
Q 23. Describe Map Reduce in Hadoop
Map Reduce in Hadoop is a software framework to process large data sets. This is the main component for data processing in the Hadoop framework. It separates the input data into numerous parts and runs a program on every data component. Map reduction is about two separate and distinct tasks. The first is the “Map” operation and the second “Reduce” operation.
Q 24. Specify the core methods of Reducer.
The core methods of a reducer in sequence are:-
a.To configure different parameters for the reducer is setup():
b.The primary operation of the reducer is reduce():
c. cleanup(): is used to clean or delete any temporary files or data after performing reduce(): task,
Q 25. Describe the default replication factor in HDFS.
The replication factor in HDFS by default is 3. The first two copies will be on the same rack and the third copy will remain off the shelf. Therefore there will be no two copies that will remain on the same data node.
Business Organisations are talent-hunting for the best and most skilled Big Data specialized personnel. The most sought-after Big Data roles are Hadoop specialists, Big Data Engineers, Data analysts, Database administrators, Data scientists etc. Prepare well for the Big Data interview questions by rehearsing the above question & answers to make your entry into this coveted profession.
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