100+ Big Data Interview Questions and Answers 2024

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. In this blog, we'll dive into some of the most commonly asked  big data interview questions and provide concise and informative answers to help you ace your next  big data job interview. Get ready to expand your knowledge and take your big data career to the next level!

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“Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe, or tap shapes business decisions. Everything is about data these days. Data is information, and information is power.” 

• Radi, data analyst at CENTOGENE.

The Big data market was worth USD 162.6 Billion in 2021 and is likely to reach USD 273.4 billion by 2026 at a CAGR of 11.10%. Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns.  

Big data analytics analyzes structured and unstructured data to generate meaningful insights based on changing market trends, hidden patterns, and correlations. Most leading companies use big data analytical tools to enhance business decisions and increase revenues. Thus, it is clear that the demand for big data analytics will keep increasing, resulting in the growth of job opportunities across the globe. 

Big Data Interview Questions and Answers 

Big data is a growing career in today’s industry. But the concern is - how do you become a big data professional? What are the questions commonly asked in a big data interview? Thus, to help you with a one-stop solution, this blog on 100+ big data interview questions and answers covers the most likely asked interview questions on big data based on experience level, job role, tools, and technologies. Also, you will find some interesting big data interview questions based on top companies such as TCS, Cognizant, and Accenture. So, let’s dive in! 

Big Data Interview Questions and Answers for Freshers

If you are a fresher appearing for a big data interview, the recruiter will ask the most basic big data interview questions for freshers like “What is Big Data?” or “What are the 7 V’s of Big Data?”. They ask such questions to know your understanding of the fundamental concepts of big data. So, let’s go through some of the most basic interview questions on big data to help you prepare and ace your upcoming big data interview. 

1. Define Big Data and Explain the Seven Vs of Big Data.

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected. Big data also enables businesses to make more informed business decisions.

The seven Vs of big data are 

• Volume: Volume represents the amount of data growing exponentially. Example: Petabytes and Exabytes. 

• Velocity: Velocity represents the rate at which the data is growing. 

• Variety: Variety refers to the data types in various data formats, including text, audio, and videos. 

• Value: Value refers to deriving valuable insights to meet business needs and generate revenues. 

• Veracity: Veracity relates to the accuracy of the analyzed data. It refers to how reliable the data is or, to put it another way, the quality of the data analyzed. 

• Visualization: Visualization refers to the presentation of data to the management for decision-making purposes. 

• Variability: Variability refers to the data that keeps on changing constantly. 

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2. How to deploy a Big Data Model? Mention the key steps.

There are three steps involved in the deployment of a big data model: 

• Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. 

• Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes.

• Data Processing: This is the final step in deploying a big data model. Typically, data processing is done using frameworks such as Hadoop, Spark, MapReduce, Flink, and Pig, to mention a few.

3. How is Hadoop related to Big Data?

Hadoop is an open-source framework for storing, analyzing, and interpreting large amounts of unstructured data to gain valuable insights and information to provide better business decisions.

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4. Explain the difference between Hadoop and RDBMS.

5. Name a few big data processing techniques. 

Big Data Processing Techniques include

• Big Data Stream Processing 

• Big Data Batch Processing

• Real-Time Big Data Processing

6. What is an "outlier" in the context of big data?

Outliers are data points that are very distant from the group and do not belong to any clusters or groups. The presence of outliers impacts model behavior; they predict the wrong outcomes or make them exceedingly inaccurate. They may also lead to misleading a machine learning or big data model. Outliers, however, may sometimes include helpful information. As a result, they must be adequately researched and studied.

7. What do you mean by commodity hardware?

Commodity hardware is the fundamental hardware resource required to operate the Apache Hadoop framework. It is a common term for affordable devices generally compatible with other low-cost devices.

8. Define and describe FSCK.

FSCK stands for File System Check, used by HDFS.  It checks whether any files are corrupt, have copies, or are missing any blocks. FSCK generates a summary report that covers the file system's overall health. For instance, HDFS receives a 

notification if any file blocks are missing with this command. Unlike the typical FSCK utility tool in Hadoop, FSCK only checks for errors in the system and does not correct them. 

9. Name the Port Numbers for NameNode, Task Tracker, and Job Tracker.

NameNode – Port 50070

Job Tracker – Port 50030

Task Tracker – Port 50060

10. What do you understand by indexing in HDFS?

HDFS indexes data blocks according to their size. The end of a data block points to the location of the next chunk of data blocks. DataNodes store data blocks, whereas NameNodes store these data blocks.

Big Data Interview Questions and Answers for Experienced Professionals 

The following section covers the big data interview questions for experienced professionals based on advanced concepts such as big data challenges, data processing techniques, and frameworks. 

11. Explain overfitting in big data. How to avoid the same.

Overfitting is a modeling error that arises when a function is tightly fitted by a limited number of data points. It produces an excessively complex model, making it even more challenging to explain the quirks or peculiarities in the data. 

Overfitting reduces the predictability of such models. This effect decreases generalization ability, leading it to fail to generalize when applied outside of the sample data.

There are several methods to avoid overfitting. Some of them are listed below: 

• Cross-validation: This method divides the data into many small test data sets that may be used to modify the model.

• Regularization: This method penalizes all parameters except the intercept so that the model generalizes the data rather than overfitting.

• Early stopping: After a certain number of iterations, the model's generalizing capacity declines; to avoid this, a procedure known as early stopping is used to prevent Overfitting before the model reaches that point.

12. What is a Zookeeper? What are the benefits of using a zookeeper?

A Zookeeper is a centralized data repository that enables distributed applications to store and retrieve data. It maintains the scattered system running as a single unit by utilizing its synchronization, serialization, and coordination goals.

Hadoop's ability to divide and conquer with zookeepers is its unique method for solving big data challenges. The solution depends on using distributed and parallel processing techniques throughout the Hadoop cluster after dividing the problem. Hadoop uses zookeeper to manage all the components of these distributed applications.

There are several benefits of using a zookeeper: 

• Atomicity: No data transfer occurs partially; it either completes successfully or fails.

• Reliability: The entire system does not collapse if a single node or a few systems fail.

• Synchronization: Collaboration and mutual exclusion between server processes. Apache HBase benefits from this process for managing configuration.

• Simple distributed coordination process: The coordination process across all Zookeeper nodes is simple.

• Serialization: Serialization is the process of encoding data according to specific rules. Make sure that your program operates consistently.

• Organized Messages: Zookeeper tracks messages with a number by marking their order with the stamping of each update; messages are ordered here with the help of all of this.

13. Explain the types of Zookeeper nodes.

Zookeeper nodes are classified as persistent, ephemeral, or sequential.

• Persistent: The default znode in the zookeeper permanently remains in the zookeeper server unless any other clients remove it. 

• Ephemeral: These are the temporary zookeeper nodes. They get deleted when the client logs off the ZooKeeper server. 

• Sequential: Sequential znodes can be either ephemeral or persistent. When a new znode is created as a sequential znode, ZooKeeper assigns the znode's path by inserting a 10-digit sequence number into the original name.

14. What is MapReduce in Hadoop?

MapReduce is a Hadoop framework used for processing large datasets. Another name for it is a programming model that enables us to process big datasets across computer clusters. This program allows for distributed data storage, simplifying complex processing and vast amounts of data.

The MapReduce program works in two different phases: Map and Reduce. Map tasks deal with mapping and data splitting, whereas Reduce tasks shuffle and reduce data. 

Hadoop can execute MapReduce applications in various languages, including Java, Ruby, Python, and C++. Map Reduce programs in cloud computing are parallel, making them ideal for executing large-scale data processing across multiple machines in a cluster.

15. When to use MapReduce with Big Data.

MapReduce is suitable for iterative computing involving massive amounts of data that must be processed in parallel. It is also appropriate for large-scale graph analysis.

16. Explain Feature Selection.

Big data may contain a large amount of data that is not necessary during processing. Thus, we may be required to choose only certain specific aspects we are interested in. Feature selection refers to extracting only the essential features from Big data. 

Feature selection Methods include -

• Filters Method: We only consider a feature's importance and usefulness in this variable ranking method.

• Wrappers Method: This method employs the 'induction algorithm,' which may be used to generate a classifier.

• Embedded Method: This method combines the benefits of both the Filters and Wrapper methods.

17. Mention the core methods of Reducer.

There are three core methods of a reducer: 

setup() - Configure various parameters such as distributed cache, heap size, and input data.

reduce() -  A parameter called once per key with the relevant reduce task.

cleaning() - Clears all temporary files and is only executed at the end of a reducer task. 

18. What is partitioning in Hive?

Partitioning in Hive means dividing the table into sections based on the values of a specific column, such as date, city, course, or country. These divisions are then further subdivided into buckets to structure the data that may be used for more efficient querying. Partitioning can reduce query response time since data is kept in slices.

19. What are the configuration parameters in a “MapReduce” framework?

The configuration parameters in a MapReduce framework are 

• Input location of Jobs in the distributed file system

• Output location of Jobs in the distributed file system

• The input format of the data

• The output format of the data

• The Class, including the Map function

• The Class, including the Reduce function

• JAR file, which contains the Mapper, Reducer, and driver classes.

Big Data Interview Questions and Answers Based on Job Role

With the help of ProjectPro experts, we have compiled a list of interview questions on big data based on several job roles, including big data tester, big data developer, big data architect, and big data engineer. These interview questions with relevant answers will help you get a competitive edge over other candidates in an interview. 

Big Data Tester Interview Questions and Answers

Following are the most likely asked big data interview questions for testers that will help you get flexibility over the concepts of big data testing and data quality checking. So, explore the questions listed below to crack any interview for the role of big data tester. 

20. How is data quality being tested?

When testing big data, the data quality is as important as processing power. The database must be examined as part of the testing process to confirm the quality of the data. It entails evaluating several characteristics, including conformity, perfection, repeatability, dependability, validity, data completeness, etc.

21.  What are the types of big data testing? 

The following are the types of big data testing: 

• Functional Testing: Big data applications with operational and analytical components need extensive functional testing at the API level. It cests for all sub-components, scripts, programs, and tools for storing, loading, and processing applicomprises tations.

• Database Testing: As the name implies, this testing often entails validating data acquired from numerous databases. It ensures that the data collected from cloud sources or local databases is complete and accurate.

• Performance Testing: Automation in big data helps you to evaluate performance under many circumstances, such as testing the application with various data types and volumes. One essential big data testing technique is performance testing, which guarantees that the components involved provide adequate storage, processing, and retrieval capabilities for large datasets.

• Architecture Testing: This testing verifies that data processing is proper and fulfills business requirements. Furthermore, if the architecture is inadequate, it may result in performance degradation, causing data processing to be interrupted and data loss to occur.

22.  List a few benefits of big data testing. 

The following are the benefits of big data testing: 

• Enhanced market targeting and strategies

• Quality cost 

• Minimizes losses and increases revenue

• Improved Business Decisions

• Data accuracy and validation  

23. What are the common challenges in performance testing?

Big data is a combination of several technologies. Each sub-element belongs to a different piece of equipment and must be tested separately. The following are some significant challenges encountered in validating Big Data:

• Diverse set of technologies: Each sub-component belongs to a different technology and must be tested separately.

• Scripting: Designing test cases necessitates a high level of scripting expertise.

• Limited availability of specified tools: A single device cannot perform end-to-end testing. NoSQL, for example, may not be appropriate for message queues.

• Test environment: A specialized testing environment is required due to the large data size.  

• Monitoring Solution: Very limited solutions exist to monitor the entire environment. 

• Diagnostic Solution: A custom solution is required to design and remove the bottleneck to improve performance.

24.  What is the difference between Big Data Testing vs. Traditional Database Testing? 

25. What is Query Surge?

Query Surge is one of the Big Data testing solutions. It maintains data quality and the shared data testing approach, which detects bad data during testing and provides an excellent perspective of data health. It ensures that the data retrieved from the sources remains intact on the target by analyzing and detecting differences in Big Data when necessary.

26. What Benefits do Query Surge provides?

Query Surge provides the following benefits: 

• Enhances testing speeds thousands of times while covering the entire data set.

• Query Surge helps us automate our manual efforts in Big Data testing. It tests several platforms such as Hadoop, Teradata, Oracle, Microsoft, IBM, MongoDB, Cloudera, Amazon, and other Hadoop suppliers. 

• It also provides automated email reports with dashboards that show data status.

• Provides an excellent return on investment (ROI) of up to 1,500%. 

27. What is Big Data Testing?

Big Data refers to a large collection of structured and unstructured data that is difficult to process using traditional database and software techniques. The volume of data in many businesses is large, and it moves too fast in modern times beyond the present processing capacity. Compilation of databases that cannot be handled efficiently by traditional computer techniques. To manage these large amounts of data, testing necessitates using specific tools, frameworks, and processes. Big data analysis refers to the generation of data and its storage, retrieval of data, and analysis of large data in terms of volume and speed variation. 

28. What is the goal of A/B testing?

A/B testing is a comparison research in which two or more page versions are shown to random users, and their comments are statistically examined to determine which version works better.

Big Data Developer Interview Questions and Answers

Let us now explore the list of important interview questions on big data for the role of big data developer. 

29. Explain distributed Cache in the MapReduce framework.

The Hadoop MapReduce architecture has a Distributed Cache feature that allows applications to cache files. Every map/reduce action carried out by the Hadoop framework on the data nodes has access to cached files. As a result, the data files in the task assigned can access the cache file as a local file.

30. Why is HDFS only suitable for large data sets and not the correct tool for many small files?

This is due to a NameNode performance problem. NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. NameNode does not use the entire space for small files, which is a performance optimization problem. 

31. Name some outlier detection techniques.

Below are six outlier detection techniques: 

• Extreme Value Analysis: This method identifies the statistical tails of a data distribution. Statistical approaches such as 'z-scores' on univariate data show powerful value analysis. 

• Linear Models: This method reduces the dimensionality of the data.

• Probabilistic and Statistical Models: This method determines the 'unlikely examples' of data from a 'probabilistic model.' Optimizing Gaussian mixture models using 'expectation-maximization' is an excellent example.

• High-Dimensional Outlier Detection: This method identifies outlier subspaces based on distance measures in higher dimensions.

• Proximity-based Models: In this technique, the data instances separated from the data group are determined using Cluster, Density, or the Nearest Neighbor Analysis.

• Information-Theoretic Models: This technique aims to find outliers as the bad data instances that increase the dataset's complexity. 

32. What is Sequencefileinputformat?

In Hadoop, SequenceFileInputFormat is an InputFormat that reads sequence files. Binary files that hold sequences of binary key-value pairs are known as sequence files. Sequence files are block-compressed and provide the direct serialization and deserialization of various data formats. 

33. Explain the NameNode recovery process.

The following are the steps to follow in a NameNode recovery process: 

• Launch a new NameNode using the FsImage (the file system metadata replica).

• Set up DataNodes and Clients together so they can recognize and use the newly launched NameNode.

• The newly launched NameNode will be prepared to begin serving the client once it has finished loading the final checkpoint of the FsImage loading process (which has now received enough block reports from the DataNodes).

However, only smaller clusters can support a NameNode's recovery process. The recovery process often takes considerable time for big Hadoop clusters, making it difficult.

34. What is the command to format the NameNode? 

The command to format the NameNode is $ hdfs namenode -format. 

35.  What role does Hadoop CLASSPATH play in starting and stopping Hadoop daemons?

CLASSPATH comprises folders containing jar files to start and stop Hadoop daemons. As a result, specifying CLASSPATH is required to start or stop Hadoop daemons.

36.  Give examples of active and passive Namenodes.

Active NameNodes work and operate inside a cluster, but Passive NameNodes have data similar to Active NameNodes.

37. What commands can you use to start and stop all the Hadoop daemons?

To start all Hadoop daemons, run the following command: ./sbin/start-all.sh

To stop all Hadoop daemons, run the following command: ./sbin/stop-all.sh

38.   How do you deal with missing values in Big Data?

Missing values are values that do not exist in a column. It occurs when a variable in observation has no data value. If missing values are not handled appropriately, they will eventually result in erroneous data, generating wrong results. As a result, it is strongly advised to address missing values appropriately before processing the datasets. If the number of missing values is small, the data is usually deleted, but if there are many missing values, data imputation is the preferable method.

There are several methods for estimating missing values in statistics. Some examples include regression, listwise/pairwise deletion, multiple data imputation, maximum likelihood estimation, and approximate Bayesian bootstrap.

39.  What is the command to copy data from the local system onto HDFS?

The command for copying data from the Local system to HDFS is:

hadoop fs –copyFromLocal [source][destination]. 

Big Data Architect Interview Questions and Answers

Following are the interview questions for big data architects that will help you ace your next job interview. 

40. Explain the data preparation process.

Data preparation is one of the essential steps in a big data project. It involves cleaning the raw data before analyzing and processing it. Data preparation is a crucial stage before processing and frequently necessitates reformatting data, enhancing data, and consolidating data sets. 

For example, standardizing data formats, improving source data, and/or reducing outliers are part of the data preparation process.

41. How do “reducers” communicate with each other?

The "MapReduce" programming model restricts "reducers" from communicating with one another, and "Reducers" operate independently.

42.  What are the components of Apache HBase?

HBase architecture has three main components: HMaster, Region server, and Zookeeper. 

• HMaster: HMaster is the HBase implementation of the Master Server. It is a process in which regions are assigned to region servers and DDL operations. HMaster has several features, such as load balancing management, failover, etc. 

• Region Server: Region Servers are the nodes at the end nodes that handle all user requests. A single Region Server has several regions. These regions include all of the rows that fall between the specified keys. Handling user requests is a complex operation. Hence Region Servers are further divided into four distinct components to facilitate request management.

• Zookeeper: Zookeeper is similar to an HBase coordinator. It offers services such as configuration information maintenance, naming, distributed synchronization, server failure notification, etc. Zookeeper is used by clients to connect with region servers.

43.  What is a UDF?

A user-defined function (UDF) is a common feature of programming languages, and the primary tool programmers use to build applications using reusable code. Because programs are primarily composed of code written by the programmer, or in this case, the user, the majority is made up of user-defined functions, sometimes punctuated by built-in functions.

So, if some functions are not accessible in built-in operators, we may programmatically build User Defined Functions (UDF) in other languages such as Java, Python, Ruby, and so on and embed them in Script files.

44. How would you check whether your NameNode is working or not?

There are numerous methods for determining the status of the NameNode. The jps command is commonly used to check the status of all daemons operating in the HDFS.

45. How is big data analysis helpful in increasing business revenue?

Big Data Analysis helps companies transform raw data into valuable and actionable insights that can be used to define business strategies. Big Data's most significant contribution to the business is data-driven business decisions, and organizations may now base their decisions on concrete facts and insights due to Big Data.

Furthermore, Predictive Analytics enables businesses to create personalized recommendations and marketing plans for various customer profiles. Big Data tools and technology work together to increase revenue, streamline corporate processes, increase efficiency, and enhance consumer satisfaction.

46. Steps for Data preparation.

Data preparation contains a series of steps: 

Step 1: Data Collection

The process starts with gathering relevant data from multiple sources, ensuring that the data collected meets the business needs and is a suitable fit for analysis. 

Step 2: Data Discovery

After collecting the data, each dataset must be identified. This process involves learning to understand the data and determining what needs to be done before the data becomes useful in a specific context. Discovery is a big task that may be performed with the help of data visualization tools that help consumers browse their data.

Step 3: Data Cleansing 

This is one of the most critical data preparation steps. This step resolves the data errors and issues to build accurate datasets. Some of the significant tasks in this step include:

• Filling out missing values

• Eliminating irregularities and outliners

• Masking private or sensitive data entries.

• Transforming data to a regulated pattern.

Step 4: Data Transformation and Enrichment

Data transformation involves changing the format or value inputs to achieve a specific result or to make the data more understandable to a larger audience. Enriching data entails connecting it to other related data to produce deeper insights.

Step 5: Data Validation

This is the last step involved in the process of data preparation. In this step, automated procedures are used for the data to verify its accuracy, consistency, and completeness. The prepared data is then stored in a data warehouse or a similar repository.  

Big Data Engineer Interview Questions and Answers

Here are a few interview questions on big data for the role of a big data engineer that prospective employers might ask you in a job interview: 

47. What is a Distributed Cache? What are its benefits?

A distributed cache is a feature provided by the MapReduce framework in Hadoop. It is a popular method to cache files when needed by the applications. It can cache read-only text files, jars files, archives, etc. Once a file has been cached, Apache Hadoop will make it available on all data nodes where map/reduce tasks are running. Thus, accessing files from any data node in a MapReduce operation becomes easy.

The following are the benefits of a distributed caching method: 

• It helps with reduced network costs.

• It distributes simple, read-only text/data files and more complex types such as jars, archives, etc.

• It monitors the modification timestamps of cache files, highlighting those that should not be changed until a job is completed successfully.

48. How is HDFS different from traditional NFS?

49. What does a Hadoop Distributed File System (HDFS) block mean?

HDFS files are divided into block-sized segments known as data blocks, which are stored as independent units. By default, the size of these HDFS data blocks is 128 MB. Hadoop distributes these blocks to different slave machines and the master machine stores information about the location of the blocks.

50. Explain the role of a JobTracker.

JobTracker is a Hadoop JVM process that submits and tracks MapReduce tasks.

The JobTracker performs the following tasks in a Hadoop: 

• JobTracker monitors the TaskTracker nodes. 

• JobTracker communicates with the NameNode to locate the data. 

• JobTracker receives client requests for MapReduce execution.

• JobTracker chooses the best TaskTracker nodes for job execution based on data locality and available slots on a given node.

51. Why do we use HDFS for applications having large data sets and not with small files? 

HDFS is best suitable to store large amounts of data in a single file than small amounts of data spread over multiple files. As you may know, the NameNode keeps metadata about the file system in RAM. As a result, the quantity of RAM limits the number of files in the HDFS file system. In other words, having too many files will lead to the generation of too much metadata. And storing these metadata in RAM will become problematic. Metadata for a file, block, or directory typically takes 150 bytes.

52. What is the main differencebetween distCP and Sqoop?

DistCP is used to transfer data between clusters, whereas Sqoop is only used to transfer data between Hadoop and RDBMS.

53. What is a Backup Node?

The Backup Node is a Checkpoint Node enhanced to support both Checkpointing and Online Streaming of File System Edits. It forces synchronization with Namenode and works in the same way as Checkpoint. The backup Node maintains the file system namespace in memory, and the backup Node must keep the current state in memory to build a new checkpoint in an image file. 

Scenario-Based Interview Questions on Big Data

Recruiters also ask scenario-based interview questions to get an idea of your knowledge and understanding of the concepts, foundations, skills, and expertise. The following are the most likely-asked scenario-based interview questions on big data. 

54. What is a block in HDFS, and its default size in Hadoop 1 and Hadoop 2? Can we change the block size?

The smallest continuous data storage unit on a hard disc is a block, and blocks are stored across the Hadoop cluster for HDFS.

• Hadoop 1's default block size is 64 MB.

• Hadoop 2's default block size is 128 MB.

Yes, it is possible to change the block size using the parameter  – dfs.block.size located in the hdfs-site.xml file. 

55. When is it appropriate to use a NoSQL database?

When working with large amounts of data, NoSQL databases are an excellent choice. They give the flexibility and scalability required for maintaining complex datasets and the ability to query and analyze data quickly without defining a schema. 

NoSQL is often the best choice when working with unstructured or semi-structured data. This database provides more flexible data storage and retrieval than typical relational databases. It also performs better when dealing with large amounts of data since it can quickly scale up and down according to your needs. Finally, NoSQL databases are frequently used in real-time analytics applications, such as streaming data from IoT sensors.

56. What happens when multiple clients attempt to write on the same HDFS file simultaneously?

Multiple users cannot simultaneously write to the same HDFS file. Because HDFS NameNode enables exclusive write, input from the second user will be rejected while the first user is accessing the file.

57. How can you get a hdfs file into a local directory?

This can be done using the following command: 

Hadoop fs – get hdfsdir local dir

58. Assume you just have one Hbase table. Is it possible to build a hive table on top of it? Any changes in the HBase table should be reflected in the hive table; this should not be a manual data transfer operation without any modifications or data movement.

Yes, we can do it by building a hive table that points to HBase as a data source. In that case, any changes to the data in HBase will be updated in the hive. When building a table, we must use the Hbase storage handler.

Big Data Interview Questions and Answers Based on Tool/Language 

Employers hire professionals with a solid understanding of big data tools and technologies. Hence, knowledge of all the big data tools and frameworks is something that can help you fetch a job. This section covers the interview questions on big data based on various tools and languages, including Python, AWS, SQL, and Hadoop. So, let’s dive in straight into Hadoop interview questions and answers. 

Hadoop Big Data Interview Questions and Answers 

The following are the interview questions on big data Hadoop, explicitly focusing on the Hadoop framework, given its widespread adoption and the ability to address big data challenges while meeting crucial business requirements. 

59. Explain the importance of Hadoop technology in Big data analytics.

The lack of analytic tools makes exploring and analyzing large unstructured data sets difficult in most circumstances. This is where Hadoop comes in since it provides storage, processing, and data collection features. Hadoop keeps data in its raw form, without any format, and allows for the addition of an unlimited number of nodes. Since Hadoop is open-source and runs on commodity hardware, it is also cost-effective for businesses and organizations to use for Big Data Analytics.

60. Explain the core components of Hadoop.

Hadoop is a framework for storing and managing large amounts of data that uses distributed storage and parallel processing. There are three components of Hadoop: 

Hadoop HDFS (Hadoop Distributed File System): HDFS is the primary storage unit that stores large amounts of data. It is primarily intended for storing large datasets on commodity hardware.

Hadoop MapReduce: A processing unit requests to process structured and unstructured data already stored in HDFS. Processing is divided into two stages: Map and Reduce. A Map is a stage in which data blocks are read and made accessible to executors for processing, and reduce is the stage in which all processed data is gathered and compiled. 

Hadoop YARN: YARN (Yet Another Resource Negotiator) is a resource management unit. It enables various data processing engines to manage data stored on a single platform, including batch processing, real-time streaming, and data science.

61. What are the three modes that Hadoop can run?

Hadoop runs in the three operating modes listed below: 

Standalone (Local) Mode - Hadoop, by default, operates in a single, non-distributed node local mode. This mode performs input and output operations using the local file system. This mode does not support HDFS, so it is only used for debugging. 

Pseudo-Distributed Mode - Similar to the Standalone mode, Hadoop operates in the pseudo-distributed mode on a single node. Each daemon runs in a separate Java process in this mode, and all the master and slave services run on a single node. 

Fully-Distributed Mode - Each daemon runs on its separate node in the fully-distributed mode, forming a multi-node cluster. For Master and Slave nodes, there are different nodes.

62. What are Edge Nodes in Hadoop?

Edge nodes are the gateway nodes in Hadoop that serve as the interface between the Hadoop cluster and the external network. They run client apps and cluster management tools in Hadoop. And are used as staging areas for data transfers to the Hadoop cluster. Edge Nodes must have enterprise-class storage capabilities (such as 900GB SAS Drives with Raid HDD Controllers), and a single edge node is generally sufficient for several Hadoop clusters.

63.  What are the common input formats in Hadoop? 

The following are the common input formats in Hadoop: 

1. Text input format: It is the default InputFormat of MapReduce. TextInputFormat considers each input file line as a single record and performs no processing. This is helpful for unformatted data or line-based records such as log files.  

2. Key value input format: It is similar to TextInputFormat in that each input line is treated as a single record. KeyValueTextInputFormat divides the line into key and value by a tab character ('/t').

3. Sequence file input format: SequenceFileInputFormat in Hadoop is an InputFormat that reads sequence files. Binary files that hold sequences of binary key-value pairs are known as sequence files.

64. What are the steps to achieve security in Hadoop?

Hadoop uses Kerberos to achieve security.  There are three steps to accessing a service while using Kerberos, including communication with a server. 

• Authentication - This first step involves authenticating the client to the authentication server, which then issues a time-stamped TGT (Ticket-Granting Ticket) to the client. 

• Authorization - In this phase, the client uses the received TGT to request a service ticket from the TGS (Ticket Granting Server).

• Service Request - This is the final stage in achieving Hadoop security. The client then uses the service ticket to authenticate himself with the server.

65.  How is Hadoop different from other parallel computing systems? 

Hadoop is a distributed file system that can store massive amounts of data and handle data redundancy over a network of computers. The primary benefit is that distributed processing is preferable since data is stored across several nodes. Instead of wasting time sending data across the network, each node may process the data stored there.

On the other hand, a relational database computer system allows for real-time data querying but storing large amounts of data in tables, records, and columns is inefficient. 

Spark Big Data Interview Questions and Answers 

Here are the most likely asked interview questions on big data spark for candidates aspiring to build a career in big data. 

66. How is Apache Spark different from MapReduce?

67. Explain the Spark architecture for big data processing. 

Spark Architecture has three major components: API, Data Storage, and Management Framework. 

• API: The API enables application developers to build Spark-based apps using a standard API interface. Spark provides APIs for the programming languages Java, Scala, and Python.

• Data Storage: Spark stores data using the HDFS file system. Any Hadoop-compatible data source, such as  HDFS, HBase, and Cassandra, etc., may be used with it.

• Resource Management: Spark may be deployed as a standalone server or as part of a distributed computing framework such as YARN or Mesos.

68. How does Spark store data? 

For data storage, Apache Spark uses the HDFS file system. It is compatible with any Hadoop data source, including HDFS, HBase, Cassandra, and Amazon S3. 

69. What does RDD mean?

RDD stands for Resilient Distributed Datasets. It is a key component of the spark framework, which can store data. Spark stores data in RDDs on several partitions. Each dataset in RDD is divided into logical divisions that may be computed on multiple cluster nodes. RDDs may contain any Python, Java, or Scala object, including user-defined classes.

An RDD is formally defined as a read-only, partitioned collection of records. RDDs can be created using deterministic operations on either stable storage data or other RDDs. 

RDDs may be created in two ways: by parallelizing an existing collection in your driver program or accessing a dataset in an external storage system such as a shared file system, HDFS, or HBase. Spark uses the RDD concept to achieve faster and more efficient MapReduce operations.

70.  Does Apache Spark provide checkpoints?

Apache Spark provides checkpoints to make streaming applications more resilient to errors. There are primarily two types of checkpoints: metadata checkpoints and data checkpoints. The metadata checkpoint is used to recover from a node failure. And the data checkpoint is used for fault tolerance in HDFS.

Python Big Data Interview Questions and Answers 

According to the Stack Overflow Developers' Survey, Python is the second "most liked" language, with 73% of developers preferring it above other market languages. Thus, employers look for professionals who have expertise in Python language. Listed below are the most common big data interview questions based on Python. 

71. What makes Python a perfect choice for big data? 

Python has several libraries for working with Big Data. In terms of writing code, using Python for Big Data is much faster than any other programming language. These two factors enable developers worldwide to choose Python as the language of choice for Big Data projects. In addition, Python also makes it extremely simple to handle any data type.

The best part about Python is that there are no data limitations, and data may be processed using simple machines such as commodity hardware, your laptop, desktop, and others. Using the PyDoop module, Python may be used to build Hadoop MapReduce programs and applications that use the HDFS API for Hadoop.

72. Explain Python’s compatibility with Hadoop. 

Python and Hadoop are open-source big data platforms, so they are more compatible with Hadoop than any other programming language. Python is more prevalent among developers because of its extensive library support for Hadoop. Additionally, Python has the PyDoop Package, which offers excellent support for Hadoop.

73. What are the benefits of using the Pydoop Package?

The following are the benefits of using the Pydoop Package: 

• Access to HDFS API: The Pydoop package (Python with Hadoop) gives you access to the Hadoop HDFS API, allowing you to develop Hadoop MapReduce programs and applications. The HDFS API helps you read and write information about files, directories, and global file system properties.

• Offers MapReduce API: Pydoop offers the MapReduce API for quickly and efficiently solving complex problems. Python is the greatest programming language for big data because it can be used to build advanced data science concepts like "Counters" and "Record Readers."

74. How do you deal with large datasets in Python? 

The following are the three methods to deal with large datasets in Python: 

• Reduce memory usage by optimizing data types: Pandas will automatically infer data types unless instructed otherwise when loading data from a file. Although this often functions well, the inferred type may sometimes be optimal. Additionally, if a numerical column has missing values, the float will be assumed as the default type.

• Split data into chunks: When dealing with a large block of data that won't fit in memory, you may use Pandas' chunk size option to divide the data into smaller chunks. By selecting this option, an iterator object gets created that can be used to iterate through the various chunks and carry out filtering or analysis in the same way that one would do when loading the entire dataset.

• Use the Lazy evaluation technique: Lazy evaluation is the foundation for distributed computation frameworks like Spark and Dask. Although they were built to function on clusters, you may use them on your computer to handle massive datasets.

SQL Big Data Interview Questions and Answers 

Below are a few big data interview questions based on basic SQL concepts and queries. 

75. Is SQL Good for Big Data?

Yes, SQL may be used efficiently with big data - complex datasets outside a relational database system. However, this necessitates overcoming the latency problems of querying and processing massive data.

76. What is the difference between SQL and MySQL?

77. What do you mean by DBMS? State its different types?

A Database Management System (DBMS) is a software application that captures and analyses data by interacting with the user, applications, and database. A DBMS allows a user to interface with the database. The data in the database may be modified, retrieved, and deleted. It can be of any type, such as strings, numbers, images, etc. 

There are two types of DBMS: 

• Relational Database Management System: The data is stored in relations (tables). For example – MySQL.

• Non-Relational Database Management System:  No concept of relations, attributes, and tuples exist. For example – MongoDB. 

78. What is the difference between relational and non-relational databases? 

A relational database is a database management system in which data is stored in different tables from which it may be retrieved or reassembled under user-defined relational tables, whereas a non-relational database is not organized around tables. This type of database stores information in a large amount of unstructured or semi-structured data.

79. Write a SQL query to display the current date?

A built-in function in SQL called GetDate() returns the current timestamp/date.

80. What is the SQL query to find the nth highest salary of an employee from employee_table.

SELECT * FROM(

SELECT employee_name, salary, DENSE_RANK() 

OVER(ORDER BY salary DESC)r FROM Employee) 

WHERE r=&n;

Thus, set n = 2 to find the 2nd highest salary 

 set n = 3 to find 3rd highest salary and so on.

81. What are the different types of relationships in SQL?

The database has several types of relations:

• One-to-One - A link between two tables in which each record in one table corresponds to the maximum of one record in the other.

• One-to-Many and Many-to-One connections are the most common, in which a record in one database is linked to several records in another.

• Many-to-Many - This defines a connection that necessitates several instances on each side.      

• Self-Referencing Relationships - This is the method to use when a table has to declare a relationship with itself.

82. What are SQL comments? 

SQL comments explain the portions of SQL statements and prevent SQL statements from being executed. There are two types of SQL comments: Single line and Multi-line comments. 

• Single Line Comments start with two consecutive hyphens (–).

• Multi-line Comments start with /* and end with */.

83. What are the different types of indexes in SQL? 

There are three types of indexes in SQL, namely: Unique index, Clustered index, and Non-clustered index. 

• Unique index: The index does not allow duplicate values if the column is uniquely indexed. 

• Clustered Index: This index reorders the table's physical order and searches depending on key values. There can only be one clustered index per table.

• Non-Clustered Index: A non-clustered index does not change the physical order of the table and keeps the data in a logical order. Each table can have a large number of nonclustered indexes.

84. What is schema in SQL Server? 

A schema is a logical visual representation of a database. It builds and describes the relationships between the database's various entities. It refers to the many types of limitations that can be imposed on a database. It also discusses several kinds of data. 

Schemas are available in various shapes and sizes, and the star schema and the snowflake schema are two of the most common. Entities in a star schema are depicted as stars, whereas those in a snowflake schema are depicted as snowflakes. 

AWS Big Data Interview Questions and Answers 

Here is the list of AWS interview questions on big data that briefly reflects on the AWS tools and how they can help realize big data objectives. 

85.  How can AWS solve Big Data Challenges?

AWS offers a wide range of solutions for all development and deployment needs. The following are the fields of big data for which AWS provides solutions: 

• Data Ingestion: Data ingestion entails gathering raw data from many sources, such as logs, mobile devices, transaction records, etc. Thus, an excellent big data platform like AWS can handle the volume and diversity of data. 

• Data Storage: Any big data platform requires a scalable, long-lasting repository to store data before or after processing processes. Depending on your needs, you may also require temporary storage for data-in-transit.

• Data Processing: This is the stage at which data is transformed from its raw state into a usable format, often by sorting, aggregating, merging, and executing more complex functions and algorithms. The generated data sets are stored for additional processing or made accessible with business intelligence and data visualization tools. 

• Data Visualization: Several data visualization technologies are available that convert processed data into graphical representations for greater understanding—information is transformed into visual components such as maps, charts, and graphs.

86.  Explain the role of AWS Glue in Big Data Architecture. 

AWS Glue is a serverless data integration service that simplifies the discovery, preparation, and integration of data from many sources for analytics users. It has uses in analytics, machine learning, and application development. Additionally, it features productivity and data operations technology for creating, executing processes, and implementing business workflows. 

AWS Glue is a service that combines essential data integration features into a single service. Data discovery, contemporary ETL, cleaning, transformation, and centralized cataloguing are examples of these. AWS Glue enables users across all workloads and user types by providing flexible support for all workloads, such as ETL, ELT, and streaming in a single service.

AWS Glue also makes it simple to connect data throughout your infrastructure. It works with AWS analytics services as well as Amazon S3 data lakes. AWS Glue features user-friendly integration interfaces and job-authoring tools for all users, from developers to business users, with customized solutions for various technical skill sets.

87.  What is the difference between Amazon S3 and Amazon DynamoDB when storing Big Data? 

Amazon DynamoDB is a fully-managed NoSQL database service where data items are stored in SSDs and replicated across three availability zones. You can offload the administrative burden of running and scaling a highly available distributed database cluster using DynamoDB while only paying for what you use. On the other hand, Amazon S3 is specified as "Store and retrieve any quantity of data, at any time, from anywhere on the web.” It offers an entirely redundant data storage architecture for storing and retrieving any quantity of data from anywhere on the internet at any time.

88.  How is AWS Lambda used for Serverless Big Data Processing?

AWS Lambda is a computational service that allows you to run code without creating or managing servers. Lambda executes your code on high-availability computing infrastructure and handles all compute resource administration, including as server and operating system maintenance, capacity provisioning, automated scaling, and logging. You can use Lambda to run code for almost any application or backend service. All you have to do is provide your code in one of the languages supported by Lambda. Your code is organized into Lambda functions, and Lambda executes your process only when required and automatically grows from a few requests per day to thousands per second. You only pay for your computing time—there is no charge if your code does not run.

89.  How does AWS Data Pipeline helps with data movement and integration in a big data project?

The AWS data pipeline enables you to access your data wherever it is stored regularly, convert and analyze it at scale, and efficiently transfer the results to other AWS services. This service provides real-time data analysis and other essential data management services.

Big Data Interview Questions and Answers By Company

You might be wondering about the interview questions asked by recruiters of top companies such as TCS, Cognizant, Accenture, etc. Thus, the following sections cover the interview questions asked in previous job interviews of these companies. 

TCS Big Data Interview Questions

Here is the list of interview questions asked at TCS: 

90.  Explain rack awareness in Hadoop. 

Rack awareness is the process of helping Hadoop to understand which machine is a part of which rack and how these racks are interconnected inside the Hadoop cluster. The NameNode in a Hadoop cluster maintains a list of all the DataNodes' rack ids. Using the rack information, Namenode selects the nearest DataNode to store the data blocks. Rack awareness in a Hadoop cluster is the primary term for knowing the cluster's topology or the distribution of various data nodes throughout the racks. Rack awareness is crucial since it ensures data dependability and aids in data recovery in the case of a rack failure.

91.  Explain the difference between a dataframe and a dataset. 

92. Define a checkpoint in a database management system. 

A checkpoint in a database management system is a process that stores the database's current state on disks. This makes it possible for a system failure or crash to recover quickly. A log of each transaction that has taken place since the last checkpoint is also part of the checkpoint process. This log is used to restore the database in case of a system failure or crash. 

Cognizant Big Data Interview Questions

Here is the list of interview questions asked at Cognizant: 

93.  How does the Spark steaming work? 

Spark streaming uses a spark engine to analyze complex and big data streams and generate final stream batches. 

The data in the streams get divided into batches called DStreams, which are internally a sequence of RDDs. RDDs are processed using Spark APIs and are returned in batches.

Spark Streaming helps in the scaling of live data streams. It is one of the basic Spark API extensions and supports fault-tolerant stream processing and high throughput. Spark Streaming also offers real-time data analysis, where data processing is done in real time and quickly using the Spark Streaming platform.

94.  What are cores in spark?

Cores in Spark control the overall number of tasks an executor may perform. It is the cornerstone of the entire spark project and helps in various tasks such as scheduling, task dispatching, input and output activities, etc. The Apache Spark core provides all functions: fault tolerance, monitoring, in-memory processing, memory management, and task scheduling.

95.  What are HDFS and YARN?

Hadoop Distributed File System (HDFS) or HDS is the primary storage component of Hadoop. It uses blocks to store many forms of data in a distributed environment, and it follows the master and slave topology. 

Yarn: Yet Another Resource Negotiator, or YARN, is the program's execution system that improves MapReduce (MR). YARN is used for scheduling, queuing, and execution management systems and organizes the executions within the containers. 

96. What is the purpose of the JPS command in Hadoop?

The JBS command determines whether all Hadoop daemons are operating correctly. It primarily checks Hadoop daemons such as the NameNode, DataNode, ResourceManager, and NodeManager.

97.  What is NameNode in Hadoop?

A NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks on the DataNodes (slave nodes). The NameNode is also regarded as the HDFS cluster's single point of failure. When NameNode fails, the file system falls. A single transaction log can be stored on a separate disc image by configuring the NameNode.

Accenture Big Data Interview Questions

Here is the list of interview questions asked at Accenture: 

98.  How many NameNodes can you run on a single Hadoop cluster?

You can have only one Namenode in a single cluster. 

99. How will you restart a NameNode?

The following commands will help you restart NameNode and all daemons:

You can stop the NameNode with the ./sbin/Hadoop-daemon.sh stop NameNode command and restart it with the ./sbin/Hadoop-daemon.sh start NameNode commands. You may use the ./sbin/stop-all.sh command to stop all daemons and restart them using the ./sbin/start-all.sh command.

100. List some use cases of the Hadoop Ecosystem. 

The following are the Hadoop use cases: 

• Hadoop in Finance: Finance and IT are the most active users of Apache Hadoop since it helps banks evaluate customers and marketers for legal systems. Banks uses a cluster to develop risk models for customer portfolios. It also supports banks in keeping a more accurate risk record, which includes recording transactions and mortgage information. It may also assess the global economy and provide customer value.

• Hadoop in Healthcare: Healthcare sector is another primary user of the Hadoop framework. It helps in disease prevention, prediction, and management by tracking large-scale health indexes. However, keeping track of patient records is the primary application of Hadoop in healthcare. It supports unstructured healthcare data, which may be processed in parallel. Users may handle terabytes of data with MapReduce.

• Hadoop in Telecom: Telecommunications companies use Hadoop-powered analytics to perform predictive maintenance on their infrastructure. Big data analytics may also plan efficient network pathways and identify the best places for new cell towers or other network development. 

• Hadoop in Retail: MapReduce can predict sales and increase profits by analyzing preliminary data. It examines a transaction history and adds it to the cluster. This data may then be used to create apps to evaluate massive amounts of data.

101. Which is the best operating system to run Hadoop?

Hadoop supports Linux-based operating systems. If you're working on Windows, you can use Cloudera VMware, Oracle VirtualBox, or VMware Workstation, which comes with Hadoop preinstalled.  

102. What is spark architecture? 

The spark architecture is a framework-based open-source component that helps process massive amounts of semi-structured, unstructured, and structured data for easy analysis. The data may then be used in Apache Spark.

Ready to Ace Your Next Big Data Interview? 

The world of big data is continuously growing, resulting in exponential job opportunities for big data professionals. We hope the above-listed interview questions will help you ace your big data interview. However, you must not overlook the significance of the practical experience. 

Practical experience is one of the most crucial aspects employers often check while interviewing a candidate. Thus, working on real-world projects before you attend any job interview is significant. ProjectPro can help you get hands-on experience with over 250+ solved end-to-end projects on big data and data science in its repository. Working on these projects will help you demonstrate your skills to your interviewer and will also help you get a competitive edge over other candidates. 

FAQs on Big Data Interview Questions and Answers 

• What is Called Big Data?

Big data refers to larger, more complex data sets, particularly from new data sources. These data sets are so large that standard data processing technologies cannot handle them. However, these vast amounts of data may be leveraged to solve previously unsolvable business challenges.

• What are the 7 V's of Big Data?

The seven Vs of big data are Value, Volume, Veracity, Variability, Velocity, Variety, and Visualization. 

• Who uses big data? 

Top technology companies use big data, such as Amazon, Google, Apple, Spotify, Facebook, Instagram, Netflix, Starbucks, etc. 

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