Ace your big data job interview with our curated selection of frequently asked questions, designed to help you demonstrate your knowledge and skills in handling large data sets
Big Data is revolutionary. It has evolved the way data was collected and analyzed earlier, and it is expected to keep evolving soon. The huge volumes of data are no longer intimidating. Big Data has applicability in every industry and has contributed to the expansion of automation and Artificial Intelligence (AI) segments. Every business worldwide requires Big Data professionals to streamline their business services by managing large volumes of structured, unstructured, and semi-structured data. Since Big Data has now become mainstream, employment opportunities are immense. Employers seek professionals with a good command of the subject, hence knowing all the technicalities of the subject and strong market knowledge is something that can help you to fetch a job. This article will discuss some of the most commonly asked Big Data interview questions and their answers.
Top Big Data Interview Questions
Q1. What is Big Data?
Ans. Big Data is a set of massive data, a collection of huge in size and exponentially growing data, that cannot be managed, stored, and processed by traditional data management tools.
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Q2. What are the different types of Big Data?
Ans. There are three types of Big Data.
Structured Data – It suggests that the data can be processed, stored, and retrieved in a fixed format. It is highly organized information that can be easily assessed and stored, e.g. phone numbers, social security numbers, ZIP codes, employee information, and salaries, etc.
Unstructured Data – This refers to the data that has no specific structure or form. The most common types of unstructured data are formats like audio, video, social media posts, digital surveillance data, satellite data, etc.
Semi-structured Data – This refers to both structured and unstructured data formats and is unspecified yet important.
Q3. Are Hadoop and Big Data co-related?
Ans. Big Data is an asset, while Hadoop is an open-source software program, which accomplishes a set of goals and objectives to deal with that asset. Hadoop processes, stores, and analyses complex unstructured data sets through specific proprietary algorithms and methods to derive actionable insights. So yes, they are related but are not alike.
Q4. Why is Hadoop used in Big Data analytics?
Ans. Hadoop is an open-source framework in Java, and it processes even big volumes of data on a cluster of commodity hardware. It also allows running many exploratory data analysis tasks on full datasets, without sampling. Features that make Hadoop an essential requirement for Big Data are –
- Data collection
- Runs independently
Q5. Name some of the important tools useful for Big Data analytics.
Ans. It is one of the most commonly asked big data interview questions.
The important Big Data analytics tools are –
- Rattle GUI
Q6. What are the five ‘V’s of Big Data?
Ans. It is one of the most popular big data interview questions.
The five ‘V’s of Big data are –
Value – Value refers to the worth of the data being extracted.
Variety (Data in Many forms) – Variety explains different types of data, including text, audio, videos, photos, and PDFs, etc.
Veracity (Data in Doubt) – Veracity talks about the quality or trustworthiness and accuracy of the processed data.
Velocity (Data in Motion) – This refers to the speed at which the data is generated, collected, and analyzed.
Volume (Data at Rest) – Volume represents the volume or amount of data. Social media, mobile phones, cars, credit cards, photos, and videos majorly contribute to the volumes of data.
Q7. What are HDFS and YARN? What are their respective components?
Ans. HDFS or Hadoop Distributed File System runs on commodity hardware and is highly fault-tolerant. HDFS provides file permissions and authentication and is suitable for distributed storage and processing. It comprises three elements: NameNode, DataNode, and Secondary NameNode.
YARN is an integral part of Hadoop 2.0 and is an abbreviation for Yet Another Resource Negotiator. It is a resource management layer of Hadoop. It allows different data processing engines like graph, interactive, stream, and batch processing to run and process data stored in HDFS. ResourceManager and NodeManager are the two main components of YARN.
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Q8. What is FSCK?
Ans. FSCK or File System Check is a command used by HDFS. It checks if any file is corrupt, has its replica, or if there are some missing blocks for a file. FSCK generates a summary report, which lists the overall health of the file system.
Q9. Name some key components of a Hadoop application.
Ans. The key components of a Hadoop application are –
- Hadoop Common
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Q10. What are the different core methods of a Reducer?
Ans. There are three core methods of a reducer-
setup() – It helps to configure parameters like heap size, distributed cache, and input data size.
reduce() – Also known as once per key with the concerned reduce task. It is the heart of the reducer.
cleanup() – It is a process to clean up all the temporary files at the end of a reducer task.
Q11. What is the command for starting all the Hadoop daemons together?
Ans. The command for starting all the Hadoop daemons together is –
Q12. What are the most common input formats in Hadoop?
Ans. The most common input formats in Hadoop are –
- Key-value input format
- Sequence file input format
- Text input format
Q13. What are the different file formats that can be used in Hadoop?
Ans. File formats used with Hadoop, include –
- Sequence files
- Parquet file
Q14. What is the standard path for Hadoop Sqoop scripts?
Ans. The standard path for Hadoop Sqoop scripts is –
Q15. What is commodity hardware?
Ans. Commodity hardware is the basic resource required to run the Apache Hadoop framework. It is a common term for affordable devices, usually compatible with other such devices.
Q16. What do you mean by logistic regression?
Ans. Also known as the logit model, logistic regression is a technique to predict the binary result from a linear amalgamation of predictor variables.
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Q17. What is the goal of A/B Testing?
Ans. A/B testing is a comparative study where two or more page variants are presented before random users, and their feedback is statistically analyzed to check which variation performs better.
Q18. What is a Distributed Cache?
Ans. Distributed Cache is a dedicated service of the Hadoop MapReduce framework, which is used to cache the files whenever required by the applications. This can cache read-only text files, archives, and jar files, which can be accessed and read later on each data node where map/reduce tasks are running.
It is among the most commonly asked big data interview questions and you must read about Distributed Cache in detail.
Q19. Name the modes in which Hadoop can run.
Ans. Hadoop can run on three modes, which are –
- Standalone mode
- Pseudo Distributed mode (Single node cluster)
- Fully distributes mode (Multiple node cluster)
Q20. Name the port numbers for NameNode, Task Tracker, and Job Tracker.
Ans. NameNode – Port 50070
Task Tracker – Port 50060
Job Tracker – Port 50030
Q21. Name the most popular data management tools used with Edge Nodes in Hadoop.
Ans. The most commonly used data management tools that work with Edge Nodes in Hadoop are –
Q22. What happens when multiple clients try to write on the same HDFS file?
Ans. Multiple users cannot write on the same HDFS file at a similar time. When the first user is accessing the file, inputs from the second user will be rejected because HDFS NameNode supports exclusive write.
Q23. What do you know about collaborative filtering?
Ans. Collaborative filtering is a set of technologies that forecast which items a particular consumer would like depending on the preferences of the scores of individuals. It is nothing but the tech word for questioning individuals for suggestions.
Q24. What is a block in Hadoop Distributed File System (HDFS)?
Ans. When the file is stored in HDFS, all file system breaks down into a set of blocks, and HDFS unaware of what is stored in the file. The block size in Hadoop must be 128MB. This value can be tailored for individual files.
Q25. Name various Hadoop and YARN daemons.
Ans. Hadoop daemons –
- Secondary NameNode
Q26. What is the functionality of ‘jps’ command?
Ans. The ‘jps’ command enables us to check if the Hadoop daemons like namenode, datanode, resourcemanager, nodemanager, etc. are running on the machine.
Q27. What types of biases can happen through sampling?
Ans. Three types of biases can happen through sampling, which are –
- Survivorship bias
- Selection bias
- Under coverage bias
Q28. Define Active and Passive Namenodes.
Ans. Active NameNode runs and works in the cluster, whereas Passive NameNode has comparable data like active NameNode.
Now, let’s explore some more Big Data Interview Questions and Answers.
Q29. How will you define checkpoints?
Ans. A checkpoint is a crucial element in maintaining filesystem metadata in HDFS. It creates checkpoints of file system metadata by joining fsimage with the edit log. The new version of fsimage is named Checkpoint.
Q30. What are the major differences between “HDFS Block” and “Input Split”?
|HDFS Block||Input Split|
|Physical division of the data||Logical division of the data|
|Divides data into blocks to store the blocks together for processing||Divides the data into the input split and assign it to the mapper function for processing|
|The minimum amount of data that can be read/write||Doesn’t contain any data and is only used during data processing by MapReduce|
Q31. What is the command for checking all the tables available in a single database using Sqoop?
Ans. The command for checking all the tables available in a single database using Sqoop is –
Sqoop list-tables –connect jdbc: mysql: //localhost/user;
Q32. How do you proceed with data preparation?
Ans. Since data preparation is a critical approach to big data projects, the interviewer might be interested in knowing what path you will take up to clean and transform raw data before processing and analysis. As an answer to one of the most commonly asked big data interview questions, you should discuss the model you will be using, along with logical reasoning for it. In addition, you should also discuss how your steps would help you to ensure superior scalability and accelerated data usage.
Q33. What is the main difference between Sqoop and distCP?
Ans. DistCP is used for transferring data between clusters, while Sqoop is used for transferring data between Hadoop and RDBMS, only.
Q34. How do you transform unstructured data into structured data?
Ans. The structuring of unstructured data has been one of the essential reasons why Big Data revolutionized the data science domain. The unstructured data is transformed into structured data to ensure proper data analysis. In reply to such big data interview questions, you should first differentiate between these two data types and then discuss the methods you use to transform one form to another. Emphasize the role of machine learning in data transformation while sharing your practical experience.
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Q35. How much data is enough to get a valid outcome?
Ans. All the businesses are different and measured in different ways. Thus, you never have enough data, and there will be no right answer. The amount of data required depends on the methods you use to have an excellent chance of obtaining vital results.
Q36. Is Hadoop different from other parallel computing systems? How?
Ans. Yes, it is. Hadoop is a distributed file system. It allows us to store and manage large amounts of data in a cloud of machines, managing data redundancy.
The main benefit is that since the data is stored in multiple nodes, it is better to process it in a distributed way. Each node can process the data stored on it instead of wasting time moving the data across the network.
In contrast, in a relational database computing system, we can query data in real time, but storing data in tables, records, and columns is inefficient when the data is huge.
Hadoop also provides a schema for building a column database with Hadoop HBase for run-time queries on rows.
Q37. What is a Backup Node?
Ans. Backup Node is an extended checkpoint node for performing checkpointing and supporting the online streaming of file system edits. Its functionality is similar to Checkpoint, and it forces synchronization with NameNode. Backup Node maintains an up-to-date in-memory copy of the file system namespace. The backup node must save the current state in memory to an image file to create a new checkpoint.
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Q38. What are the common data challenges?
Ans. The most common data challenges are –
- Ensuring data integrity
- Achieving a 360-degree view
- Safeguarding user privacy
- Taking the right business action with real-time resonance
Q39. How would you overcome those data challenges?
Ans. Data challenges can be overcome by –
- Adopting data management tools that provide a clear view of data assessment
- Using tools to remove any low-quality data
- Auditing data from time to time to ensure user privacy is safeguarded
- Using AI-powered tools, or software as a service (SaaS) products to combine datasets and make them usable
Q40. What is the Hierarchical Clustering Algorithm?
Ans. The hierarchical grouping algorithm is the one that combines and divides the groups that already exist. In this way they create a hierarchical structure that presents the order in which the groups are split or merged.
Q41. What is K-mean clustering?
Ans. K mean clustering is a method of vector quantization. With this method, objects are classified as belonging to one of the K groups, which are selected as a priori.
Q42. What is n-gram?
Ans. N-gram is a continuous sequence of n elements of a given voice or text. The N-gram is a probabilistic language model used to predict the next item in the sequence in the form of (n-1).
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Q43. Can you mention the criteria for a good data model?
Ans. A good data model –
- It should be easily consumed
- Large data changes should be scalable
- Should offer predictable performances
- Should adapt to changes in requirements
Q44. What is the bias-variance tradeoff?
Ans. It is the bias that represents the precision of a model. A model with a high bias tends to be oversimplified and results in insufficient fit. The variance represents the sensitivity of the model to data and noise. A model with high variance results in overfitting.
Therefore, the trade-off between bias and variance is a property of machine learning models in which lower variance leads to higher bias and vice versa. In general, an optimal balance of the two can be found in which error is minimized.
Q45. Tell me how to select a sample from a population of product users randomly.
Ans. A technique called simple random sampling can be used to select a sample from a population of product users randomly. Simple random sampling is an unbiased technique that randomly takes a subset of individuals, each with an equal probability of being chosen, from a larger data set. It is usually done without replacement.
In the case of using a library like pandas, you can use the .sample () to perform simple random sampling.
Q46. Describe how gradient augmentation works.
Ans. Gradient augmentation is an ensemble method similar to AdaBoost, which essentially iteratively builds and enhances previously constructed trees using gradients in the loss function. The final model predictions are the weighted sum of the predictions from all previous models.
Q47. What is the Central Limit Theorem (CLT)? How would you determine if the distribution is normal?
Ans. The central limit theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases regardless of the shape of the population distribution.
Q48. What is ‘cross-validation’?
Ans. It is among the most popular big data interview questions.
Cross-validation can be difficult to explain, especially in an easy and understandable way.
Cross-validation analyses whether an object can function as expected once it is used on live servers. In other words, it looks at how certain specific statistical analysis results are valued when an independent data set is put in.
Q49. What is the difference between ‘expected value’ and ‘average value’?
Ans. There is no difference between ‘expected value’ and ‘average value’ when it comes to functionality; there is no difference between the two. However, they are used in different situations.
An expected value usually reflects random variables, while the average value reflects the population sample.
Q50. What is ‘cluster sampling’?
Ans. Cluster sampling is a method that helps the researcher divide the population into separate groups called clusters. Then a simple cluster sample is selected from the population and the data is analyzed from the sample clusters.
Q51. Explain the steps to deploy a Big Data solution.
Ans. Followings steps are followed to deploy a Big Data Solution –
- Data Ingestion: It is the first step in the big data solution deployment. The data is extracted from various resources such as Salesforce, SAP, MySQL and other log files, documents, etc. Data ingestion can be performed through batch jobs or real-time streaming.
- Data Storage: The extracted data is either stored in HDFS or NoSQL database (i.e. HBase). HDFS storage is used for sequential access, while HBase is used for random read/write access.
- Data Processing: This is the final step in big data solution deployment. Data is processed through a framework such as Pig, Spark, MapReduce, etc.
Q52. What is the difference between Network-attached storage (NAS) and HDFS?
Ans. NAS has a lesser chance of data redundancy since its replication protocol is different runs and as it runs on an individual machine. HDFS runs on a cluster of machines, due to which data redundancy is frequent in HDFS. While data is stored in dedicated hardware in NAS, data is stored as data blocks in local drives in HDFS.
These big data interview questions and answers will help you get your dream job. You can always learn and develop new Big Data skills by taking one of the best Big Data courses.
Which are the top industries hiring big data professionals?
Some of the top industries hiring big data professionals are Banking, Financial Services, and Insurance (BFSI); Information Technology; Healthcare; Pharmaceuticals; Hospitality; Education; Entertainment; Energy; Automotive; and E-Commerce.
What job profiles are available in the big data field?
Some of the popular job profiles and titles available in the big data field are Data Analyst; Data Engineer; Big Data Visualizer; Systems Architect; Big Data Researcher; Data Warehouse Manager; Data Architect; Database Developer; Business System Analyst; Data Strategist; Business Intelligence Specialist. u2022 Statistician
What is the average salary of big data professionals in India?
As per Glassdoor, the average salary of a Big Data Engineer in India is Rs. 7,78,607 per year.
What skills are needed for big data jobs?
The top skills required for big data jobs are Knowledge of SQL; Knowledge of Data Warehousing; Familiarity with Big Data Tools; Programming Skills: Experience in languages, such as Java, Python, and R; Strong foundation in Statistics; Data Visualization Skills; Quantitative Aptitude; Familiarity with Business Domain; Strong Analytical Skills; Problem Solving Skills.
What are the major roles and responsibilities of a big data professional?
Big data professionals are responsible for a variety of tasks, which include Programming of Hadoop applications; Designing the architecture of a big data platform; Developing, implementing, and maintaining data pipeline; Customizing and managing integration tools, warehouses, and databases; Creating scalable and high-performance web services; Managing and structuring data; Analyze a large number of data stores and uncovering insights.
Is it hard to get a big data job?
The demand for big data professionals across the industries is high yet getting a job in this field could be challenging. It could be because of different reasons, such as increased competition or lack of understanding of companies of what they need. However, irrespective of why you are not able to get a job right now, if you can focus on developing and enhancing your big data skills and gaining enough hands-on experience, you will be able to achieve your dream job.
What are the educational requirements to become a big data professional?
To become a big data professional, you must have an undergraduate or postgraduate degree in a relevant discipline, such as mathematics, science, engineering, economics, statistics, or a business-related field.
How do I start a career in big data with no experience?
To start a career in big data with no experience, you can build fundamental big data skills and knowledge such as data warehousing, big data tools, modeling, integration, processing. You can explore online Big Data courses on Naukri Learning to learn about the fundamental as well as advanced concepts in big data to get started.
Which are the best online courses to learn big data?
Below are some of the top online courses to learn big data: u2022 Big Data Specialization on Coursera u2022 Big Data Computing by NPTEL u2022 Big Data Analytics on FutureLearn u2022 Big Data Analytics: Opportunities, Challenges and the Future on FutureLearn u2022 Big Data Analytics with Tableau on Pluralsight u2022 Big Data & Reporting with MongoDB on Pluralsight u2022 BigData Modeling and Management Systems on Coursera
Does big data require programming skills?
Programming is an essential skill for big data analysts. As a big data professional, you will be required to code to perform numerical and statistical analysis with large-scale datasets. To do so, you will need to know programming languages such as Java, Python, R, and C++.
Which is the best tool for big data?
Some of the popular Big Data tools in 2021 are Apache Hadoop, MongoDB, Apache Spark, OpenRefine, Flink, Apache Storm, Apache Cassandra, MongoDB, Kafka, Datawrapper, Talend, and Tableau.
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