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Table of contents
1.
Introduction
2.
Big Data Interview Questions for Freshers
2.1.
1. What is Big Data?
2.2.
2. Mention some prominent uses of Big Data.
2.3.
3. State the different approaches to dealing with Big Data.
2.4.
4. Explain in brief the five V’s of Big Data.
2.5.
5. Mention the differences between Big Data Testing and Traditional Database Testing.
2.6.
6. How do you see Big Data Testing? Name some tools for Big Data.
2.7.
7. What do you understand by the term Collaborative Filtering?
2.8.
8. What are the test parameters to be looked after while Big Data Testing?
2.9.
9. What is Clustering?
2.10.
10. How would you convert unstructured data to structured data?
2.11.
11. State the needs of the Test Environment.
2.12.
12. How can we test the quality of data being processed?
2.13.
13. Throw some light on the distributed cache.
2.14.
14. Define Big Data Analytics.
2.15.
15. What are the challenges faced by Big Data Analysts?
3.
Big Data Interview Questions for Experienced
3.1.
16. Briefly explain the types of Big Data Analytics.
3.2.
17. How can you increase business revenue using Big Data Analysis?
3.3.
18. What is Architecture Testing?
3.4.
19. Explain Performance Testing.
3.5.
20. State the different tools of Automated Data Testing available.
3.6.
21. What are the components of Query Surge’s architecture?
3.7.
22. Do you have any idea about Query Surge?
3.8.
23. State the benefits that come along with Query Surge.
3.9.
24. Define Persistent, Ephemeral, and Sequential znodes.
3.10.
25. What are the steps involved in deploying a Big Data solution?
3.11.
26. What are Outliers?
3.12.
27. Mention the different core methods of a Reducer.
3.13.
28. Explain with the help of diagram distcp.
3.14.
29. Define Data Staging.
3.15.
30. State the differences between NAS and HDFS.
4.
Conclusion
Last Updated: May 24, 2024
Easy

Big Data Testing Interview Questions

Author Rupal Saluja
0 upvote
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Speaker
Ashwin Goyal
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18 Jun, 2024 @ 01:30 PM

Introduction

Big Data is a thing of the present and future. It has been a long struggle for businesses to find an approach that captures and analyzes their customers, products, and services. This approach is a competitive advantage for those who know how to properly utilize Big Data.

If you are looking for a job in data testing, you must consider becoming a Data Analyst. This quick guide on Big Data Testing Interview Questions will help a lot if you prepare for an interview regarding the same in the future. This article consists of the 30 most popular Big Data Testing Interview Questions, so let’s get started without wasting any time.

Big Data Testing Interview Questions

Big Data Interview Questions for Freshers

1. What is Big Data?

Ans: Any data source with at least three characteristics, the 3V’s - Extremely high Volumes, Extremely high Velocity, and Extremely wide Variety of data can be considered Big Data.

Big Data should not be confused as a single technology, but it is a combination of old and new technologies. It can be said to have the capability to manage enormous amounts of data at the right speed and within the right timeframe. Thus, allowing us to perform real-time analysis.

2. Mention some prominent uses of Big Data.

Ans: Uses of Big Data:

  • Targeted Ads for compelling consumers based on past purchases, search histories, and viewing histories.
  • Improved and informed decision-making is something that has led to a competitive advantage for companies with excellent data management capabilities.
  • Risk Management is yet another use of Big Data Analytics. New risks are identified from previous data patterns.
  • Big Data Analytics also helps with product development by providing insights on development decisions and progress measurement.
  • Consumer data help companies design their marketing strategies, which can act on trends and may lead to customer satisfaction.

3. State the different approaches to dealing with Big Data.

Ans: There are two approaches as far as Big Data processing is concerned.

  • Batch Processing
  • Stream Processing

The business requirements, associated concerns, and the available budget decide the approach to be determined for Big Data Processing.

4. Explain in brief the five V’s of Big Data.

Ans: The five V’s of Big Data are explained below.

  • Volume – Represents the amount of data.
  • Velocity – The rate at which data grows.
  • Variety – Refers to the different data types.
  • Veracity – Refers to the uncertainty of available data. 
  • Value –Term used for revenue generated by data.

5. Mention the differences between Big Data Testing and Traditional Database Testing.

Ans: To look into the differences between Big Data Testing and Traditional Database Testing, refer to the table below.

Traditional Database Testing

Big Data Testing

The validating tool required is excel based on macros or automotive tools with UI.There are no specific and definitive tools.
Tools are very simple and do not require any specialized skills.A Big Data Tester must be trained specially, requiring continuous updates.
The data is structured and compact.Big Data includes structured as well as unstructured data.
Methods are time-tested and properly defined.Methods are developing and are still improving continuously. Big Data requires R&D efforts too.


6. How do you see Big Data Testing? Name some tools for Big Data.

Ans: Big Data Testing includes advanced tools, specialized frameworks, and efficient methods to handle enormous amounts of datasets. Big data testing covers everything from the creation of data, storage, retrieval, and analysis to providing suggestions and feedback.

Some of the most commonly used tools for Big Data are MongoDB, MapReduce, Cassandra, Apache Hadoop, Apache Pig, Apache Spark, and many more.

7. What do you understand by the term Collaborative Filtering?

Ans: Collaborative Filtering refers to the cluster of technologies that forecast and predict which items a selected customer would like. This filtering is done depending on the preferences of the individuals.

8. What are the test parameters to be looked after while Big Data Testing?

Ans: The test parameters to be looked into while testing Big Data are listed below.

  • Message Queue
  • Map-Reduce
  • JVM parameters
  • Timeouts
  • Caching
  • Concurrency
  • Logs
  • Data Storage

9. What is Clustering?

Ans: Grouping of similar kinds of objects into a set, known as a cluster. It is one of the essential steps in data mining. Some popular clustering methods are hierarchical, partitioning, density-based, model-based, etc.

10. How would you convert unstructured data to structured data?

Ans: There can be many ways to convert unstructured data to structured data. The two most prominent ways are mentioned below.

  • Programming

Programming is the most popular method used for transforming unstructured data into a compact and structured format. You can change the data in any form using several programming languages, such as Python, Java, C++, C, etc.

  • Data or Business Tools

Several Business Intelligence tools support drag-and-drop functionality that helps them convert unstructured data into structured data. The drawback is that most of these tools are paid for, and an individual has to be financially capable before using these tools.

11. State the needs of the Test Environment.

Ans: The most suitable test environment depends on the nature of the application. The various needs of the Test Environment are mentioned below.

  • Appropriate space for processing, excluding a significant amount of storage space for test data.
  • Efficient CPU Utilization and Minimum Memory are required for performance maximization.
  • The data inside the scattered cluster.

12. How can we test the quality of data being processed?

Ans: Data quality becomes an important factor when it comes to Big Data Testing. Since the amount of data is massive, its quality must be up to the mark.

Data Quality Testing is a part of the examination of the database. It includes inspecting various factors like perfection, repetition, reliability, validity, completeness of data, etc.

13. Throw some light on the distributed cache.

Ans: Distributed Cache is a specialized service dedicated to the Hadoop MapReduce framework. It is used to cache the read-only text files, archives, and jar files whenever required. These files can be accessed and read later on each data node where the map or reduced tasks are running.

14. Define Big Data Analytics.

Ans: Big Data Analytics is gathering, Storing, Managing, and Manipulating massive amounts of data at the right pace, at the right time, to gain the right insights.

It is a complex process that involves uncovering hidden patterns and helping firms to make informed decisions that result in commendable outcomes. Competitive advantage why Big Data Analytics has gained popularity among various departments.

15. What are the challenges faced by Big Data Analysts?

Ans: Some of the challenges faced by Big Data Analyst are mentioned below.

  1. Many data come unstructured in pictures, videos, etc., making it difficult for analyzers to manage.
  2. Creating new sources such as sensors, click-stream, smart devices, etc., now and then requires an evolution of technology, which takes time.
  3. A lot of data in various forms requires extremely good data quality maintenance.
  4. The complexity of the Big Data ecosystem leads to several security concerns which must be addressed appropriately.
  5. The high cost of hiring experienced data analysts and engineers and the lack of potential analytical skills in every other individual make it difficult for some firms to go with the pace.
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Big Data Interview Questions for Experienced

16. Briefly explain the types of Big Data Analytics.

Ans: Majorly, there are four types of Big Data Analytics described below in the table.

Type of AnalyticsExplanation
Descriptive AnalyticsDescriptive analytics focuses on summarizing historical data to understand what happened in the past. It provides insights into trends, patterns, and key performance indicators.
Diagnostic AnalyticsDiagnostic analytics aims to determine why certain events occurred by analyzing historical data. It helps identify root causes of issues or anomalies observed in the data.
Predictive AnalyticsPredictive analytics uses historical data and statistical algorithms to forecast future outcomes or trends. It helps organizations anticipate potential future scenarios.
Prescriptive AnalyticsPrescriptive analytics goes beyond predicting future outcomes by recommending actions to achieve desired outcomes. It suggests the best course of action based on data analysis.

17. How can you increase business revenue using Big Data Analysis?

Ans: Nowadays, effective big data analysis plays a major role in businesses. It distinguishes them from others and helps them increase their revenue. Big Data Analytics provides customized recommendations and suggestions, new products can be launched based on consumer preferences, calculated predictions can be made, and detailed insights into the root cause can be documented.

These factors help the business make more money and increase its worth significantly in the market.

18. What is Architecture Testing?

Ans: By its name, it is clear that Architecture Testing has something to do with the architecture of the system. The Big Data Architecture includes Data sources, Data storage, Batch Processing, Real-time message ingestion, Stream Processing, and Analytical Datastore. The Architecture Testing checks whether its components are fully functional and efficient or not.

A loosely planned system results in performance degradation, and the complete system might not meet the planned expectations of the firm. That is where architecture testing comes into play.

19. Explain Performance Testing.

Ans: The term performance includes duration to complete the job, memory utilization, data throughput, and parallel system metrics. So, performance testing includes keeping a track of the above parameters on which the overall performance of the system depends.

Performance Testing of Big Data consists of two functions, Data Ingestion and Data Processing. The factors mentioned above influence the efficiency of these two functions primarily.

20. State the different tools of Automated Data Testing available.

Ans: The different tools available for Automated Big Data Testing are listed below.

  • Big Data Testing
  • ETL Testing & Data Warehouse
  • Testing of Data Migration
  • Enterprise Application Testing / Data Interface /
  • Database Upgrade Testing

21. What are the components of Query Surge’s architecture?

Ans: The several components of Query Surge’s architecture are mentioned below.

  • The Query Surge Application Server

This is the first component of Query Surge’s architecture. An application server helps in creating web applications and an environment to run those applications. Tomcat works very well as an application server.

  • The Query Surge Database

The Query Surge Database contains the data which will be processed throughout. You can use MySQL to manage the databases.

  • Query Surge Agents

The surge agents are the mid-components that differ based on the objective of the project. It is mandatory to use at least one agent in a complete process.

  • Query Surge Execution API

This component is optional. The Execution API is the final step that completes the Query Surge’s architecture and leads to manual execution. If this step is not completed manually, it is performed by default at the end.

22. Do you have any idea about Query Surge?

Ans: Query Surge is one solution for Big Data Testing. It ensures that the data from various sources stay intact by continuously examining the differences and pinpointing wherever necessary. This enhances the data quality and shared data testing method that helps in Bad Data detection and gives amazing health to data.

23. State the benefits that come along with Query Surge.

Ans: The benefits that come along with the Query Surge are mentioned below.

  • Query Surge helps in Efforts’ Automation. This offers a test to be done across diversified platforms such as HortonWorks, MapR, DataStax, Hadoop, Teradata, MongoDB, Oracle, Microsoft, IBM, Cloudera, Amazon, and other Hadoop vendors like Excel, flat files, XML, etc.
  • Testing speeds enhancement by more than thousands of times while offering complete data coverage.
  • Continuous Delivery of integrated DevOps solution.
  • Automated reports through email stating data health.
  • Excellent Return on Investments (ROI).

24. Define Persistent, Ephemeral, and Sequential znodes.

Ans: Persistent znodes

It is the permanent and default znode in ZooKeeper. It stays in the ZooKeeper until any other client leaves it apart.

Ephemeral znodes

It is the temporary znode and is smashed whenever the client logs out of the ZooKeeper server.

Sequential znodes

A Znode assigned a 10-digit number in numerical order at the end of its name is known as a Sequential Znode.

25. What are the steps involved in deploying a Big Data solution?

Ans: As you are familiar with the term Big Data, you must be knowing how difficult is to manage it. Companies have been using Big Data to increase their revenue and generate suggestions for their production. There is no specific solution to deploying a Big Data solution. The three common steps which are rolled every time when deploying a Big Data Testing solution are explained below.

  1. Data Ingestion

Extraction of data from several sources is termed Data Ingestion. We can ingest the extracted data through batch jobs or real-time streaming.

2. Data Storage

We can store the extracted data either in HDFS or NoSQL database.

3. Data Processing

The data can be processed using one of the frameworks available, like Spark, MapReduce, Pig, etc.

Big Data solution

Steps of Deploying Big Data Solution

26. What are Outliers?

Ans: Outliers are data points that are far from the cluster and not part of it. Their behavior is quite different from the data group, so, they are not included in the cluster. Also, this distinguished behavior affects the behavior of the model resulting in wrong results prediction. Also, their accuracy is very low. Still, they must be handled carefully as they might contain some useful information. Their presence might be misleading to a Big Data Model or a Machine Learning Model.

Big Data Model

27. Mention the different core methods of a Reducer.

Ans: The three different core methods of a Reducer are listed below.

  • setup()

This method helps configure parameters such as heap size, distributed cache, and input data size.

  • reduce()

This method is the heart of the reducer. It is called once per key with the concerned reduced task.

  • cleanup()

This method contains a process to clean up all the temporary files after the reducer task ends.

28. Explain with the help of diagram distcp.

Ans: A Tool used for copying a huge amount of data to and from Hadoop file systems in parallel is known as distcp. To affect data distribution, error handling, recovery, and reporting, it uses MapReduce. To understand distcp better, refer to the diagram below.

diagram distcp

29. Define Data Staging.

Ans: Data Staging refers to adding stages involved in the data processing process. It involves every step which comes between the data source and data target. The initial stage is validation. Data from several sources like social media, RDBMS, etc., are validated to get accurate data uploaded. The next stage is process verification. We compare the data source with the uploaded data into HDFS to ensure that both match. Lastly, we should confirm that the correct data has been pulled.

30. State the differences between NAS and HDFS.

Ans: To look into the differences between NAS and HDFS, refer to the table below.

ParametersNASHDFS
ArchitectureCentralized architecture with a single storage deviceDistributed architecture with multiple storage nodes
ScalabilityLimited scalability, typically vertical scalingHighly scalable, designed for horizontal scaling
Data AccessFile-based access via standard protocols (e.g., NFS)Block-based access optimized for parallel processing
Data StorageData stored on a dedicated storage deviceData distributed across multiple nodes in a Hadoop cluster
Fault ToleranceRelies on redundancy and RAID for fault toleranceBuilt-in fault tolerance through data replication across multiple nodes
PerformancePerformance may degrade under heavy workloadsOptimized for high throughput and parallel processing
Metadata ManagementCentralized metadata managementDistributed metadata management within the Hadoop cluster
Use CasesSuitable for smaller-scale environments and traditional file servingIdeal for big data processing and analytics, especially in Hadoop ecosystems

Conclusion

We hope you have gained insights on Big Data Testing Interview Questions through this article. We hope this will help you excel in your interviews and enhance your knowledge of Big Data Testing. These Big Data Testing Interview Questions and answers are suitable for freshers and experienced candidates.

This set of Big Data Testing interview questions will not only help you in interviews but also would increase your understanding of the topic.

Here are some of the interview questions on some of the famous topics:

 

For peeps who want to grasp more knowledge other than Big Data Testing interview questions, that is, DBMSOperating SystemSystem Design, and DSA, refer to the respective links. Make sure that you enroll in the courses we provide, take mock tests, solve problems available, and interview puzzles.  Also, you can pay attention to interview stuff- interview experiences and an interview bundle for placement preparations. Do upvote our blog to help other ninjas grow.

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