Businesses across the globe are increasingly seeing the wisdom of embracing the notion of big data. The careful analysis and synthesis of massive data sets can provide invaluable insights to help them make informed and timely strategic business decisions. For example, knowing what new products to develop based on a deep understanding of customer behaviors, preferences and buying patterns. It can also shed light on where untapped potential may be found, such as new territories or nontraditional market segments.
As the race to augment businesses' big data capabilities and skills intensifies, the demand for qualified candidates in the field is reaching new heights. If you have aspirations to pursue a career path in this domain, a world of opportunity awaits. Today's most challenging -- yet rewarding and in-demand -- big data roles include data analysts, data scientists, database administrators, big data engineers and Hadoop specialists. Knowing what the likely big data interview questions an interviewer will ask and how to answer such questions is essential to success.
In this article, we provide some direction to help set you up for success in your next big data interview -- whether you are a recent graduate in data science/information management or already have experience working in big data-related roles or other technology fields. We step you through some of the most commonly asked interview questions related to big data that prospective employers might ask.
Let's get started.
How do I prepare for a big data interview?
Before delving into the specific questions and answers associated with big data job interviews, let's begin with the basics of interview preparation:
- Prepare a tailored and compelling resume. Ideally, you should tailor your resume (and cover letter) to the particular role for which you are applying. These documents should demonstrate not just your qualifications and experience, but be geared toward convincing your prospective employer you've undertaken some background research into their business' history, financials, strategy, leadership, culture and vision as well. Also, don't be shy to call out what you believe to be your strongest 'soft skills' that would be relevant to the role. These may include communication and presentation capabilities; tenacity, an eye for detail and professionalism; and respect, teamwork and collaboration.
- Remember, an interview's a two-way street. Correctly and articulately providing answers to your interviewers' technical questions is essential, of course. But don't overlook the value of posing your own questions to the interviewers. Prepare a shortlist of these questions in advance of the appointment to ask them at opportune moments.
- The Q&A: prepare, prepare, prepare. Investing time upfront in to research and prepare your answers to the most commonly asked questions and rehearse them is essential. Be yourself during the interview, however. Look for ways to bring your personality to bear and convey your responses authentically and thoughtfully. Monosyllabic, vague or bland answers won't serve you well.
Let's now turn to the top 25 big data interview questions. These include a specific focus on the Hadoop framework, given its widespread adoption and ability to solve the most difficult big data challenges, thereby delivering on core business requirements.
Top 25 big data interview questions with answers
1. Please outline your experience in big data.
If you have held previous roles in the field of big data, outline your title, duties and career path. Include any specific challenges and highlights/achievements.
2. We often speak of the four Vs of big data -- can you list them?
- Volume. The amount of data.
- Variety. The various formats of data.
- Velocity. The ever-increasing speed at which data is growing.
- Veracity. The degree of accuracy of available data.
To impress your interviewer, add two additional V terms that have entered the big data conversation of late:
- Value. The business value of the data collected.
- Variability. The ways in which the data can be used and formatted.
3. Explain how to go about deploying a big data platform. What are the key steps involved?
You can roll out a big data platform following these three steps:
- Data ingestion. Start out by collecting data from multiple sources, be they social media platforms, log files or business documentation.
- Data storage. After extracting the data, store it in a database, which could be the Hadoop Distributed File System (HDFS) or Apache HBase.
- Data processing. Typically done via frameworks such as Hadoop, Apache Spark, Flink or Pig and MapReduce, to name a few.
4. Why is Hadoop so popular in big data analytics?
Hadoop is effective in dealing with large amounts of structured, unstructured and semistructured data. Analyzing unstructured data isn't easy, but Hadoop's storage, processing and data collection capabilities make it less onerous. In addition, Hadoop is open source and runs on commodity hardware, which makes it less costly.
As a follow-on from this question, please define the following four terms, specifically in the context of Hadoop:
5. Open source
Hadoop's an open source platform, which means the code is made available and can be rewritten or have changes applied, depending on the users' needs.
Hadoop clusters allow for additional hardware resources via new nodes as needed.
7. Data recovery
Hadoop replication ensures any failure doesn't result in the loss of data.
8. Data locality
Hadoop moves computation close to where data resides (rather than the other way around).
9. Can you list the various vendor-specific distributions of Hadoop?
10. Please list Hadoop's different configuration files.
11. Define HDFS and Yet Another Resource Negotiator (YARN), and outline their respective components.
HDFS is Hadoop's default storage unit. It stores various types of data in a distributed environment. It has two key components:
- A primary node that holds the metadata information for all the data blocks in the HDFS.
- Nodes that act as secondary nodes and store the data.
Apache Hadoop YARN manages resources and provides an execution environment for the required processes.
12. How many modes can Hadoop run in?
- Standalone mode (the default mode)
- Pseudo-distributed mode
- Fully distributed mode
13. Explain the common input formats in Hadoop.
- Text input format. This is the default input format.
- Sequence file input format. Used to read files in a sequence.
- Key value input format. Used for plain text files.
14. What makes the HDFS fault-tolerant?
HDFS can replicate data on different DataNodes. So, should one node fail or crash, the data may still be accessed from one of the others.
15. Explain how to ensure security in Hadoop.
This is achieved by using Kerberos and following three basic steps (each of which involves message exchanges with a server):
- Service request
16. Can you explain the purpose of the JPS command in Hadoop?
The JPS command is used to test the workings of all the Hadoop daemons, specifically NameNode, DataNode, ResourceManager and NodeManager.
17. What commands are used to start up and shut down Hadoop daemons?
- To start all the daemons:
- To shut down all the daemons:
18. Outline the key differences between network file systems (NFS) and HDFS.
NFS is among the most popular but also one of the oldest distributed file storage systems. HDFS is a more recent technology, and is well-suited to handling big data.
19. What is MapReduce? What syntax is required to run a MapReduce program and effectively perform a MapReduce job?
MapReduce is a programming model in Hadoop that's used for processing large data sets across a cluster of computers. The syntax used to run a MapReduce program is:
20. Explain how Hadoop MapReduce operates
MapReduce programming is comprised of two main elements:
- Map phase. Here, input data is split by map tasks, which run in parallel. The split data is used for the purposes of analysis.
- Reduce phase. During this phase, the similar split data is aggregated from the collection and renders an outcome.
21. Define "outliers" in the context of big data.
An outlier is a data point that's abnormally distant from others in a group of random samples. The presence of outliers can potentially mislead the process of machine learning and result in inaccurate models or substandard outcomes. That said, outliers can sometimes contain nuggets of valuable information.
22. Name two common outlier detection techniques.
- Extreme value analysis. Determines the statistical tails of the data distribution; statistical methods like Altman Z-scores on univariate are good examples of extreme value analysis.
- Probabilistic and statistical models. Determine the unlikely instances from a probabilistic model of data.
23. Define the term FSCK.
FSCK stands for file system consistency check, a command that's used to run a Hadoop summary report, which summarizes the status of the HDFS. It merely identifies the presence of errors, however. It doesn't correct them. The FSCK command may be executed on an entire system or a select subset of files.
24. Are you open to earning advanced/additional learning and qualifications that would help you to advance your career with us?
Here's your chance to demonstrate your enthusiasm and career ambitions. Of course, your answer will depend on your current level of academic qualifications/certifications, as well as your personal circumstances, which may include family responsibilities and financial considerations. Therefore, respond forthrightly and honestly. Bear in mind that numerous courses and learning modules are readily available online. Analytics vendors have also established training courses aimed at those seeking to upskill themselves in this domain. Also, you could inquire about the company's policy on mentoring and coaching.
25. Do you have any questions for us?
As mentioned earlier, it's a good rule of thumb to come to interviews with a few preprepared questions. But depending on how the conversation has unfolded during the interview, you may choose not to ask them. For instance, if they've already been answered or the discussion has sparked other, more pertinent queries in your mind.
A final word on big data interview questions
Remember, the process doesn't end after an interview has wrapped up. After the session, send a note of thanks to the interviewer(s) or your point(s) of contact. Follow this up with a secondary message if you've not received any feedback within a few days.
The world of big data is expanding continuously and exponentially. If you're serious and passionate about the topic and prepared to roll up your sleeves and work hard, the sky's the limit.