Top 40 Big Data Interview Questions and Answers

The need for skilled workers in the industry is increasing to unprecedented levels as businesses scramble to improve their big data capabilities and expertise. This article will offer some guidance to help you prepare for success in your next big data interview through our big data interview questions and answers. Thrive your career with our big data course.

Big Data Interview Questions and Answers

Here are 40 interview questions in big data for freshers.

Interview Questions on Core Big Data Concepts

1. What is Big Data?

The term “big data” describes enormous amounts of data that are too big and complicated for conventional data processing software to handle.

Large, complicated, and varied data sets that are challenging to handle and evaluate with conventional data processing techniques are referred to as big data. Unstructured, semi-structured, and structured data may be included.  

2. What are the 5 V’s of big data?

Volume, velocity, variety, veracity, and value are the five V’s of big data.

Volume: Over time, the size of big data sets increases rapidly.

Velocity: Big data is produced quickly. In big data, velocity describes how quickly data may be created, collected, and examined. 

Variety: Many different kinds of data can be included in big data, including unstructured data like pictures, semi-structured data like XML files, and structured data like financial transactions.

Veracity: In big data, veracity refers to the data’s quality, accuracy, and dependability. It is a gauge of the degree of confidence that can be put in the gathered data.  

Value: The advantages that an organization can obtain from the data it gathers are what give big data its worth. From a business standpoint, it is the most significant feature of big data.  

3. What is Hadoop?

Large datasets can be processed and distributed across computer clusters using the open-source Hadoop framework. 

4. How does Hadoop operate?

Hadoop divides workloads into manageable, concurrently running jobs by utilizing distributed storage and parallel processing. It is quicker than using a single massive computer since it clusters several computers to evaluate data simultaneously.

• Hadoop is utilized in a wide range of sectors, such as banking, healthcare, security, and law enforcement.
• Hadoop is written in Java, but it may also be used with C, Python, and C++, among other programming languages.

5. How is data stored using Hadoop?

Hadoop stores data in blocks using the Hadoop Distributed File System (HDFS): 

• Name node: The master node that controls the data nodes and keeps track of metadata. 
• Data node: The slave nodes that read, write, process, and duplicate data are known as data nodes. 

6. How is data replicated using Hadoop?

For security, Hadoop automatically duplicates data three times. This implies that a new machine with identical data can be used instead of a commodity machine if it fails. 

7. What is HDFS?

A distributed file system called Hadoop Distributed File System (HDFS) was created to store massive volumes of data on several cluster nodes.

Large volumes of data are managed and stored by the Hadoop Distributed File System (HDFS). It is an essential part of Apache Hadoop, an open-source framework that makes it possible to handle and store massive data volumes in a distributed manner.  

8. What is MapReduce?

MapReduce is a programming technique that uses a straightforward approach to process big datasets. It is also a software framework that distributes the processing of massive volumes of data.

9. How does MapReduce work?

MapReduce divides huge data processing tasks into smaller, faster tasks by using parallel processing. There are two primary steps in the model:

• Map: Divides data into jobs that may be processed in parallel, then applies transformation logic to each piece. Key/value pairs are used to format the output.
• Reduce: Creates a final collection of key/value pairs by combining all values with the same key.  

10. What is the benefit of MapReduce?

As of its great scalability, MapReduce can operate on thousands of nodes. This lowers the price of processing and storing vast volumes of data. 

11. Where is MapReduce implemented?

Hadoop, a data analytics engine frequently used for big data, powers MapReduce. For input and output, it makes use of the Hadoop Distributed File System (HDFS).  

Example: A simple word count Each word’s frequency of appearance in a text is counted by the MapReduce process. 

• Every line of text is divided into words by the mapper, which then outputs a key/value pair for each word. 
• Each word’s counts are added up by the reducer, which then outputs a single key/value pair.  

12. What is a data warehouse?

To facilitate business intelligence (BI), reporting, and analytics, a data warehouse is a digital storage system that gathers and arranges vast volumes of data from multiple sources.

• Purpose: To convert data into insights to assist organizations in making well-informed decisions.
• Function: Serves as an organization’s sole source of truth by centrally storing both history and current data. 
• Data sources: point-of-sale transactions, marketing automation, CRM, and other systems.

13. What is a data lake?

Any amount of structured and unstructured data can be kept in one place in a data lake. 

Stores any type of data: Without initially structuring the data, data lakes can contain semi-structured, unstructured, or structured data in its original format. 

Processes data in real-time or batch mode: Both batch and real-time data processing are possible with data lakes. 

14. Define data mining.

Finding patterns in massive data sets using techniques at the nexus of database systems, statistics, and machine learning is known as data mining.

Check out our Hadoop course syllabus to learn more. 

Interview Questions in Big Data Tools and Technologies

15. What is Apache Spark?

A quick and versatile cluster computing solution is Apache Spark. Large volumes of data are processed with the open-source analytics engine Apache Spark.

• Speed: Compared to Hadoop MapReduce, Spark can complete tasks up to 100 times faster.
• Ease of use: Spark provides more than 80 high-level operators for creating parallel applications, making it easy to use.
• Flexibility: Spark may operate independently or in conjunction with Kubernetes, Apache Mesos, or Apache Hadoop.
• Scalability: Spark is capable of handling real-time analytics as well as batches.
• Code reuse: Spark facilitates the reuse of code for various tasks.
• Programming languages: R, Python, Scala, and Java are among the languages Spark offers development APIs. 

16. Define Apache Kafka.

Apache Kafka, a distributed streaming framework, was developed to handle the data processing in real-time. It is an open-source tool called Kafka that gathers, analyzes, and saves real-time streaming event data.

What it does: 

• Kafka is a platform for streaming distributed events that can:
• Manage streams of data from various sources.
• Provide info to several customers.
• Keep and examine data from the current time as well as from the past.
• Create event-driven, real-time apps.
• daily intake and processing of billions of records  

17. Explain Apache Flink.

A distributed processing engine and framework for stateful calculations across data streams is Apache Flink. Low latency processing, in-memory computations, high availability, eliminating single points of failure, and horizontal scaling are all features of Apache Flink’s design.

Use case: A well-liked tool for scalable, event-driven, high-performance applications and architectures is Apache Flink. It is employed in machine learning algorithms and data analytics.  

18. What is NoSQL?

The term “not only SQL” (or “NoSQL”) describes non-relational databases that preserve data in a format distinct from relational databases. NoSQL database systems are capable of handling large volumes of unstructured data. 

Though NoSQL databases have been around since the 1960s, their popularity has surged due to the increasing data generated by social media, mobile technology, cloud computing, and big data.  

NoSQL databases possess the following features:  

• Flexible schema: They can handle a diverse array of data types, including graphs, documents, key-value pairs, and wide columns.  
• Scalability: They can be expanded by adding more hardware components.  
• Performance: Certain data structures and access patterns are particularly well-suited for NoSQL databases.  
• Development ease: NoSQL databases are widely recognized for their ease of creation and management.
• Queries: In terms of speed, NoSQL queries often surpass traditional SQL queries.  
• Data integrity: One method that NoSQL databases employ to maintain data integrity is BASE (Basically Available, Soft State with Eventual Consistency).

19. How does SQL differ from NoSQL?

NoSQL databases are non-relational and more adaptable for unstructured data, whereas SQL databases are relational databases that handle data using structured query language. 

• Relational databases utilize a table structure, whereas NoSQL databases can be categorized as document stores, key-value pairs, graph-based, or wide-column formats. 
• SQL databases excel in handling transactions that involve multiple rows, while NoSQL databases are more suitable for managing unstructured data such as documents or JSON files.

20. What is a data pipeline?

A data pipeline refers to a series of processes that extract, transform, and load (ETL) data from a source system to a destination system. They are crucial due to their ability to remove manual processes, facilitate automated data movement, and be vital for analytics in real time. 

There are primarily two categories of data pipelines: 

• Batch processing: Utilized for analyzing historical data 
• Streaming: Allows users to capture data as it occurs 

Current trends in data pipelines consist of the growing adoption of AI and machine learning, real-time streaming pipelines, serverless and cloud-native solutions, DataOps integration, and the incorporation of edge computing.

21. How does the data pipeline work?

A data pipeline consists of a sequence of steps designed to automate the transfer and transformation of data from one source to another, aiming to make the data available for analysis.

• Data sources: Information can originate from numerous sources, such as applications, IoT devices, and tools for managing social media.  
• Data processing: Data is transferred, organized, filtered, reformatted, and analyzed.  
• Storage: Data is saved in either a data warehouse or a data lake, based on the data’s format.  
• Data consumers: The information is utilized for business intelligence reports, data visualization, or machine learning purposes.  

Our big data course syllabus covers all the concepts you need to learn. 

Machine Learning and Big Data Analytics Interview Questions

22. What is data analytics?

The practice of examining big data sets to identify trends, patterns, and correlations as well as to extract knowledge that might aid in decision-making is known as data analytics. 

It entails analyzing historical and present data using a range of methods, such as statistical analysis and machine learning, to forecast future trends. 

Helping organizations make better, data-driven decisions is the aim of data analytics. Businesses that fully benefit from data analytics create an internal data-driven culture where choices are made using facts rather than gut feelings.

23. Explain the uses of data analytics.

There are numerous business applications for data analytics, such as:

• Marketing: customer segmentation for advertising campaigns.
• Delivery logistics: finding the most economical shipping routes, streamlining procedures, and enhancing delivery times.
• Risk reduction: identifying dishonest practices in the financial sector.
• Resource allocation: distributing funds, personnel, or production capabilities.

24. Explain big data analytics.

The act of gathering, processing, and drawing conclusions from massive, fast-moving data sets employing tools, techniques, and applications is known as big data analytics. Numerous sources, including social media, email, mobile devices, the internet, and networked smart devices, can provide these data sets.

Big data analytics is helpful for the following:

• Develop business procedures
• Enhance decision-making
• Promote company expansion
• Forecast future results.
• Project client needs
• Suggest the best plan of action or approach. 

25. What are the tools and methods that can be used in big data analytics?

The following are some big data analytics tools and methods:

• Data visualization: It represents data using visual components such as maps, charts, and graphs.
  • This facilitates the processing of numerical, complicated, or high-volume data. 
• NoSQL databases: JSON documents are used to store data in non-relational databases.
  • These databases are an excellent choice for handling and storing unstructured, raw, large data. 
• Predictive analytics: Predictive analytics looks for trends that could indicate future behavior using statistical models, machine learning, artificial intelligence, and data analysis. 
• Prescriptive analytics: Prescriptive analytics analyzes data and information using sophisticated procedures and tools to suggest the best course of action or approach. 

26. What is machine learning?

One form of artificial intelligence (AI) that enables machines to learn and get better from data without explicit programming is machine learning.

• How it operates: Large data sets are analyzed by machine learning algorithms to find trends and connections, which are then used to inform predictions and choices.
• How it becomes better: As machine learning systems are exposed to additional data, their performance gradually improves.  

Machine learning algorithms come in a wide variety, although they are frequently categorized based on similarities in form, function, or learning style.

27. What are the applications of machine learning?

Healthcare, entertainment, shopping carts, and housing are just a few of the industries that use machine learning. To detect fraudulent transactions, for instance, a financial institution may employ machine learning.

By stacking algorithms on top of one another, deep learning builds intricate networks that are capable of handling increasingly difficult tasks.

Join our data science with machine learning course for further understanding. 

Interview Questions in Big Data Architecture and Deployment

28. What is meant by a Hadoop cluster? 

A Hadoop cluster is a collection of computers, referred to as nodes, that collaborate to process, store, and analyze vast volumes of data.

Finance, healthcare, retail, and telecommunications are just a few of the industries that frequently use Hadoop clusters, which are built for big data analytics. Because they can swiftly scale up or down to meet the needs of fluctuating data sets, they are ideal for these sectors.

29. What are the key features of a Hadoop cluster?

The Apache Software Foundation created the open-source Hadoop software framework.   Hadoop clusters have the following key features:

• Master-worker setup: The worker nodes process and store data under the direction of a master node.
• Distributed data: Multiple nodes replicate data, protecting it from software failure and enabling the cluster to retrieve data in the event of a computer malfunction.
• HDFS, or Hadoop Distributed File System: Files are divided into blocks using this approach and then dispersed among the cluster’s nodes.
• MapReduce: Tasks are broken up into smaller pieces, processed concurrently, and the outcomes are combined by this processing framework.
• Yet Another Negotiator for Resources (YARN): To guarantee that the cluster’s applications have the resources they require, this layer schedules and controls system resources. 
• Share nothing: All that the cluster nodes have in common is the network that links them.

30. What are a data node and a name node?

A data node is a slave node that stores the actual data in the Hadoop Distributed File System (HDFS), whereas a name node is the master server that regulates file access and maintains the file system metadata.

• Name Node: The name node, sometimes referred to as the master, is in charge of the file system namespace, which includes the filesystem tree and metadata for every file and directory. 
• Data Node: The name node instructs a data node to store the actual data. Typically, workstations with larger disks are used to run data nodes.  

31. What is HBase in big data?

Based on the Hadoop Distributed File System (HDFS), a key part of Apache Hadoop, HBase is a column-oriented, non-relational database management system. Sparse data sets are frequently used in big data use cases, and HBase offers a fault-tolerant method of storing them.

32. What is Hive in Big Data?

Using SQL, users can read, write, and manage big datasets with the Apache Hive data warehouse system.

• Built on Hadoop: Apache Hadoop, an open-source system for processing and storing massive datasets, is the foundation upon which Hive is based.  
• SQL-like Interface: To enable users to query big datasets, Hive uses HiveQL, a query language that is comparable to SQL.  
• Fault-tolerant: Large-scale analytics are made possible by the distributed, fault-tolerant Hive system. 
• Scalable: Hive is made to operate rapidly on big datasets and has the capacity to store hundreds of petabytes of data.
• Flexible: Users may quickly spin virtual servers up or down with Flexible Hive to adapt to changing workloads.
• Secure: For security and observability, Secure Hive interfaces with Apache Ranger and Apache Atlas and supports Kerberos auth. 

33. What is Pig in Big Data?

A platform called Apache Pig is used to analyze and process massive data sets. Apache Pig was created by Yahoo to simplify the analysis of big data sets without the need for intricate Java code. 

• High-level language: Pig creates data analysis programs using a scripting language known as Pig Latin. For relational database management systems, Pig Latin is comparable to SQL.
• Easy to program: Programming is made simple with Pig; users don’t need to know Java to write data transformations.
• Self-optimizing: Pig can optimize task execution on its own, freeing users to concentrate on semantics rather than efficiency.
• Extensible: To do special-purpose processing, users can design their own functions.
• Parallel execution: Pig programs are capable of handling very huge data sets since they can be significantly parallelized.  
• Runs on Hadoop: Pig may run its tasks in MapReduce, Apache Tez, or Apache Spark on Hadoop clusters. 
• Saves output in HDFS: Pig’s output is consistently stored in the Hadoop distributed file system (HDFS) for storage. 

34. What is Spark SQL?

One Spark module for structured data processing is called Spark SQL. It may function as a distributed SQL query engine and offers a programming abstraction known as DataFrames. 

It makes it possible for unaltered Hadoop Hive queries to execute on current deployments and data up to 100 times faster.

Common Spark SQL Functions:

• String Functions.
• Date & Time Functions.
• Collection Functions.
• Math Functions.
• Aggregate Functions.
• Window Functions.

35. Explain Kafka streams.

Developers can create microservices and real-time applications with the help of the client library Kafka Streams. Key features of Kafka streams are:

• Access to stream processing primitives such as filtering, grouping, aggregating, and joining is made possible by the Java API Kafka Streams. 
• It has the advantages of scalability, fault tolerance, and security because it is a native part of Apache Kafka.
• With Kafka streams, developers don’t have to worry about deployment and can concentrate on their applications. 
• Since Apache Kafka producers and consumers are abstracted over it, developers can disregard the finer points.  

How it operates: Anywhere that can connect to a Kafka broker, Kafka Streams generates a stand-alone application. Compared to code produced using the vanilla Kafka clients, the application contains fewer lines since it is declarative rather than imperative.  

Architecture: The architecture used by Kafka Streams is a graph of stream processors (nodes) connected by shared state stores or streams (edges). There are two unique processors in the topology:

• Source processor: It generates an input stream from one or more Kafka topics for its topology.
• Sink processor: Any records received from its upstream processors are sent to a designated Kafka topic by the sink processor.  

Explore all in-demand software training courses available at SLA. 

36. What is Flink SQL?

An SQL engine that complies with the ANSI standard, Flink SQL is capable of processing both historical and real-time data. It gives users a declarative means of expressing analytics and data transformations on data streams. 

• Without writing complicated code, users can quickly convert and analyze data streams with Flink SQL.
• An open-source framework for data processing, Apache Flink provides special features for batch and stream processing. 
• It is a well-liked tool for creating scalable, event-driven, high-performance applications and infrastructures. 

Interview Questions on Big Data Security and Governance

37. What are the security challenges in big data?

Big data security is a collection of instruments and safeguards that guard against theft, attacks, and malevolent activity in data and analytics operations. 

The following are some large data security challenges:

• Unauthorized access: As big data systems include complicated and sensitive data, they are appealing targets for cyberattacks.
  • Big data platforms can be used by criminals to obtain data without authorization, interfere with business operations, or result in financial loss.
• Data theft: If a company keeps sensitive or private data, such as credit card numbers or customer information, information theft can have major repercussions.
• Non-compliance: Companies that disregard fundamental data security protocols risk fines.
  • This can involve not adhering to data loss prevention and privacy regulations.
• Malicious activities: A system may crash as a result of online or offline attacks on big data systems.
  • Ransomware, DDoS attacks, and other malicious actions are examples of these attacks.

38. Explain data governance in big data.

The practice of managing an organization’s data to guarantee its accuracy, security, and usability is known as data governance. The management of vast volumes of data to aid with decision-making is known as data governance in big data.

Data governance consists of:

• Policies and procedures: Creating internal guidelines for the collection, processing, storage, and disposal of data.
• Roles and responsibilities: Specifying the people with the power and duty to handle particular kinds of data.
• Technology: Setting up the systems and tools to facilitate data governance.
• Compliance: Following external guidelines established by governmental organizations, trade associations, and other interested parties.
• Data classification: Sorting and classifying information according to its criticality, worth, and sensitivity.

39. Explain the concept of data lineage and its importance in big data.

Data lineage is the process of tracing data as it passes through the systems of an organization, including its transformation and final destination. It’s crucial in big data since it benefits businesses.

• Verify data integrity: Data lineage aids in ensuring that information is reliable and unaltered.
• Troubleshoot: As data lineage may trace errors back to their origin, it can be used to detect and correct errors.
• Comply with regulations: Organizations can prove compliance with laws such as the CCPA and GDPR by using data lineage.
• Understand impact: Data lineage can assist businesses in comprehending the effects of data changes on systems and processes downstream.
• Clean up data systems: The performance of an organization’s data systems can be enhanced by using data lineage to find and eliminate outdated, unnecessary data.  

40. Explain the concept of data partitioning and replication in HDFS.

Data partitioning and replication are employed in the Hadoop Distributed File System (HDFS) to guarantee data accessibility and dependability.

• Data partitioning: It divides a big database into smaller sections, known as partitions, and allocates a distinct node to each segment.
  • Another name for this is sharding. Data organization and retrieval effectiveness are enhanced by partitioning.
• Data replication: It makes several copies of the identical data on various nodes, maybe in various places.
  • Because of the redundancy this provides, data can still be supplied from the other nodes if some are inaccessible.  

Conclusion

Big data has emerged as a crucial element of contemporary business, allowing companies to glean insightful information from enormous datasets. An extensive review of the basic ideas, tools, and uses in the big data field has been given in these big data interview questions and answers. Shape your career with our big data training in Chennai.