Increase your confidence before your big data analytics interview by reviewing some common questions you may encounter.
Big data is changing how businesses process information and make informed decisions. According to the India Brand Equity Foundation (IBEF), this industry will likely continue to grow over the coming years as the data analytics market consistently records double-digit growth rates [1]. This growth represents promising employment opportunities. Whether looking for a new career in data science or just starting in the field, knowing how to handle questions about big data is essential.
Here are nine questions a job interviewer may ask if you apply for a job requiring big data analytics knowledge.
What they’re really asking: The interviewer wants to know that you understand how big data works and how it has changed data science technology infrastructure.
How to answer: Big data is a massive amount of data that requires scalable architecture to store, process, and model the data. It is consistently expanding, creating the need for more advanced processing and analytical tools than traditional data sets.
When answering this question in an interview, you should clearly understand big data and provide a simple explanation.
Similar Questions:
Why is big data important?
Why is a big data framework different from traditional data analysis?
What they’re really asking: The V’s of big data have become a simple way to understand how big data differs from other types of data. They are often considered the main characteristics of big data and are important to understand.
How to answer: Many organisations have different “V’s” they use to define and characterise big data. The five common V’s you should be familiar with are:
Volume: Refers to the massive, exponential growth of data being generated every day
Velocity: Refers to the rate at which data is generated and how this data moves
Variety: Refers to the many different formats for data, such as structured and unstructured data produced
Variability: Refers to the fact that big data is constantly changing, and the velocity, structure, or format may change within a data set itself
Veracity: Refers to the quality of data being collected and analysed, including how trustworthy and complete the data set is
By understanding the characteristics of big data, you demonstrate an awareness of its importance and how to work effectively with this type of data set.
Similar Questions:
What are the V’s of big data?
What are some characteristics of big data?
What they’re really asking: The interviewer is assessing how much you understand about the three types of data used in big data, which may require different cleaning and structuring techniques.
How to answer: Data comes in three types: Structured, semi-structured, and unstructured. Structured data is organised by a specific set of characteristics into a readable, accessible format like a table or spreadsheet. Semi-structured data falls somewhere between structured and unstructured. It hasn’t yet been organised into a spreadsheet or similar table, but it does have specific markers or tags that tell you what that data represents. Unstructured data has no organisation whatsoever. It typically includes images, videos, and social media posts. This data requires further processing to gain helpful insights.
Understanding the differences between each type is important since you may need to model different types of data in your position.
Similar Questions:
Why is it important to structure data?
What is the difference between unstructured data and structured data?
What they’re really asking: Will you be able to demonstrate your understanding of data storage solutions, such as Hadoop, which is an essential component for managing data.
How to answer: Data storage solutions help store, process, and manage big data. Some common frameworks and tools include Apache Hadoop, Apache Spark, Google BigQuery, and Microsoft Azure HDInsight. Hadoop is among the most popular open-source frameworks for quickly storing and processing massive amounts of data. It organises big data into a scalable architecture that provides high computing power, storage, and scalability at a low cost.
Similar Questions:
Why is Hadoop important in big data analytics?
How does a data storage framework help organise and analyse big data?
What they’re really asking: Knowing how Hadoop functions may not be enough for the interviewer. Depending on the position, they will want to know that you understand Hadoop's purpose and the tools that make Hadoop function.
How to answer: Some of the helpful modules of Hadoop are:
Hadoop common: The utilities and libraries needed to support other Hadoop modules
Hadoop Distributed File System (HDFS): A Java-based system that stores and provides access to application data from many machines
Hadoop YARN (Yet-Another Resource Negotiator): A framework for more resource management and processing
Hadoop Map Reduce: A parallel processing framework for large data sets that use a master node that distributes problems to subproblems and then distributes those into smaller worker nodes
It is important to note that many other software frameworks and components exist that can be used with Hadoop to streamline data processing into Hadoop.
Similar Questions:
What are the tools Hadoop offers to analyse big data?
What makes Hadoop helpful in analysing big data?
What they’re really asking: Similar to question five, understanding Hadoop’s functions and modes is essential to demonstrate your skills to an interviewer.
How to answer: Hadoop has three supported modes:
Standalone mode: A default mode commonly used to test and debug MapsReduce application
Pseudo-distributed mode (also known as a single node cluster): Allowscode debugging and memory use analysis via a single node that uses simulated clusters
Fully-distributed mode (also known as a multiple node cluster): A production model that distributes data amongst multiple nodes
Similar Questions:
What are the differences in Hadoop’s modes?
Why would you run one mode over another?
What they’re really asking: An interviewer will want to see if you understand the tools and processes used to clean, analyse, and make use of big data. This demonstrates that big data is often useless without data mining and machine learning methods.
How to answer: Data mining and machine learning are essential analytical techniques used to organise, analyse, and visualise patterns from big data.
While data mining and machine learning are two separate processes, they often work in tandem. Data mining generates patterns found in data, and machine learning takes those patterns and makes predictions of future probabilities.
Finding patterns in data and making predictions based on those patterns are essential to understanding big data. This is also an excellent opportunity to describe any data mining or machine learning techniques you have experience with.
Similar Questions:
How does data mining differ from machine learning?
How do we organise, analyse, and visualise big data in a useful way?
What they’re really asking: Big data functions through programming languages, and an interviewer will want to know which ones you are comfortable using.
How to answer: As with any career in data science, it is essential to understand different programming languages and how they support big data analytics. Some of the most used programming languages when dealing with big data include:
Python
SQL
R
VBA (Visual Basic for Applications)
Julia
Java
SAS
MATLAB
SCALA
JavaScript
C/C++
While you may not need to know every programming language, understanding Python and SQL may be especially useful when working with big data. You can use Python for many tasks, including machine learning applications, and SQL is an essential language to understand to query, analyse, and manipulate structured data in relational databases. It can integrate with Python or R to perform deeper analysis.
Similar Questions:
What programming languages do you know?
How do you use programming to understand and analyse big data?
What they’re really asking: This question will depend on the type of position you are applying for. For business-related positions, an interviewer will likely want to know how you can implement an extensive data framework in their specific field, so study the company and industry you are applying for.
How to answer: Big data modelling is another tool for businesses or individuals to make confident, analytical decisions in their industry and predict future trends. Data models offer a visual representation of data systems essential to predicting trends, understanding behaviours, and optimising business operations.
Similar Questions:
How would you apply big data analytics in our industry?
How is big data used to make business decisions?
Build skills in big data analytics with courses on Coursera, such as the Big Data Specialisation offered by the University of California San Diego. To learn more about interviewing techniques and stand out in your next interview, consider brushing up on your skills with online courses such as Successful Interviewing by the University of Maryland, also available on Coursera.
IBEF. “Scope of Data Analytics in India and Future, https://www.ibef.org/blogs/scope-of-data-analytics-in-india-and-future.” Accessed 17 December 2024.
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