https://www.techtarget.com/whatis/feature/Top-25-big-data-interview-questions-with-answers
Increasingly, organizations worldwide see the wisdom of embracing big data. The careful analysis and synthesis of massive data sets provide invaluable insights to help them make informed and timely strategic business decisions.
For example, big data analytics help determine what new products to develop based on a deep understanding of customer behaviors, preferences and buying patterns. Analytics can also reveal untapped potential, such as new territories or nontraditional market segments.
As organizations race to augment their big data capabilities and skills, the demand for qualified candidates in the field is reaching new heights. If you aspire 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 big data questions an interviewer is likely to ask -- and how to answer -- is essential to success.
This article offers direction for your next big data interview, whether you are a recent graduate in data science or information management or you already have experience working in big data-related roles or other technology fields. This piece also provides some of the most common big data interview questions that prospective employers might ask.
Before delving into the specific big data interview questions and answers, here are the basics of interview preparation.
Now, here are the top 35 big data interview questions. These include a specific focus on the widely adopted Hadoop framework, given its ability to solve the most difficult big data challenges, thereby delivering on core business requirements.
Each of the following 35 big data interview questions includes an answer. Don't rely solely on these answers when preparing for your interview. Instead, use them as a launching point for digging more deeply into each topic.
As basic as this question is, have a clear and concise answer that demonstrates your understanding of this term and its full scope, making it clear that big data includes just about any type of data from any number of sources, including the following:
Big data can include structured, semi-structured and unstructured data -- in any combination -- collected from a range of heterogeneous sources. Once collected, the data must be carefully managed so it can be mined for information and transformed into actionable insights. When mining data, data scientists and other professionals often use advanced technologies such as machine learning, deep learning, predictive modeling or other advanced analytics to gain a deeper understanding of the data.
There are a number of ways that big data can benefit organizations, as long as they can extract value from the data, gain actionable insights and put those insights to work. Although you won't be expected to list every possible outcome of a big data project, be ready to cite several examples that demonstrate an effective big data project achievement. Include any of the following:
Big data analytics aids organizations within specific industries. For example, a utility company might use big data to better track and manage electrical grids. Or governments might use big data to improve emergency response, reduce crime and support smart city initiatives.
If you have had previous roles in the field of big data, outline your title, functions, responsibilities and career path. Include any specific challenges and how you met those challenges. Also mention any highlights or achievements related either to a specific big data project or to big data in general. Be sure to include any programming languages you've worked with, especially as they pertain to big data.
No big data project is without its challenges. Some of those challenges might be specific to the project itself or to big data in general. You should be aware of what some of these challenges are -- even if you haven't experienced them yourself. Below are some of the more common challenges:
Big data is often discussed in terms of the following five Vs:
Interviewers might ask for only four Vs, rather than five. In which case, they're usually looking for the first four (volume, variety, velocity and veracity). If this happens in your interview, you might also mention that there is sometimes a fifth V: value. To impress your interviewer even further, you can mention yet another V: variability, which refers to the ways in which the data can be used and formatted.
There is no one formula that defines exactly how a big data platform should be implemented. However, it's generally accepted that rolling out a big data platform follows these three basic steps:
Hadoop is an open source distributed processing framework for handling large data sets across computer clusters. It can scale up to thousands of machines, each supporting local computation and storage. Hadoop can process large amounts of different data types and distribute the workloads across multiple nodes, which makes it a good fit for big data initiatives.
The Hadoop platform includes the following four primary modules (components):
Hadoop is effective in dealing with large amounts of structured, unstructured and semi-structured 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, so it is less costly than systems that rely on proprietary hardware and software.
One of Hadoop's biggest selling points is that it can scale up to support thousands of hardware nodes. Its use of HDFS facilitates rapid data access across all nodes in a cluster, and its inherent fault tolerance makes it possible for applications to continue to run even if individual nodes fail. Hadoop also stores data in its raw form, without imposing any schemas. This allows each team to decide later how to process and filter the data, based on their specific requirements at the time.
As a follow-on from this question, please define the following four terms, specifically in the context of Hadoop:
Hadoop is an Open Source platform. As a result, users can access and modify the source code to meet their specific needs. Hadoop is licensed under Apache License 2.0, which grants users a "perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form." Because Hadoop is open source and has been so widely implemented, it has a large and active user community for helping to resolve issues and improving the product.
Hadoop can be scaled out to support thousands of hardware nodes, using only commodity hardware. Organizations can start out with smaller systems and then scale out by adding more nodes to their clusters. They can also scale up by adding resources to the individual nodes. This scalability makes it possible to ingest, store and process the vast amounts of data typical of a big data initiative.
Hadoop replication provides built-in fault tolerance capabilities that protect against system failure. Even if a node fails, applications can keep running while avoiding any loss of data. HDFS stores files in blocks that are replicated to ensure fault tolerance, helping to improve both reliability and performance. Administrators can configure block sizes and replication factors on a per-file basis.
Hadoop moves the computation close to where data resides, rather than moving large sets of data to computation. This helps to reduce network congestion while improving the overall throughput.
Several vendors now offer Hadoop-based products. Some of the more notable products include the following:
The Hadoop platform provides multiple configuration files for controlling cluster settings, including the following:
HDFS is a distributed file system that serves as Hadoop's default storage environment. It can run on low-cost commodity hardware, while providing a high degree of fault tolerance. HDFS stores the various types of data in a distributed environment that offers high throughput to applications with large data sets. HDFS is deployed in a primary/secondary architecture, with each cluster supporting the following two primary node types:
Hadoop YARN manages resources and provides an execution environment for required processes, while allocating system resources to the applications running in the cluster. It also handles job scheduling and monitoring. YARN decouples resource management and scheduling from the data processing component in MapReduce.
YARN separates resource management and job scheduling into the following two daemons:
Hadoop supports three primary operational nodes.
Hadoop supports multiple input formats, which determine the shape of the data when it is collected into the Hadoop platform. The following input formats are three of the most common:
Hadoop supports other input formats as well, so maintain a good understanding of them as well.
HDFS easily replicates data to different DataNodes. HDFS breaks files down into blocks that are distributed across nodes in the cluster. Each block is also replicated to other nodes. If one node fails, the other nodes take over, letting applications to access the data through one of the backup nodes.
Rack awareness is one of the mechanisms used by Hadoop to optimize data access when processing client read and write requests. When a request comes in, the NameNode identifies and selects the nearest DataNodes, preferably those on the same rack or on nearby racks. Rack awareness can help improve performance and reliability, while reducing network traffic. Rack awareness can also play a role in fault tolerance. For example, the NameNode places data block replicas on separate racks to ensure continued access in case a network switch fails or a rack becomes unavailable for other reasons.
Hadoop uses the Kerberos network authentication protocol to protect data from unauthorized access. Kerberos uses secret-key cryptography to provide strong authentication for client/server applications. A client must undergo the following three basic steps -- each of which involves message exchanges with the server -- to prove its identity to that server:
Speculative execution is an optimization technique that Hadoop uses when it detects that a DataNode is executing a task too slowly. There can be many reasons for a slowdown, and it can be difficult to determine its actual cause. Rather than trying to diagnose and fix the problem, Hadoop identifies the task in question and launches an equivalent task -- the speculative task -- as a backup. If the original task completes before the speculative task, Hadoop kills that speculative task.
JPS, which is short for Java Virtual Machine Process Status, is a command used to check the status of the Hadoop daemons, specifically NameNode, DataNode, ResourceManager and NodeManager. Administrators can use the command to verify whether the daemons are up and running. The tool returns the process ID and process name of each Java Virtual Machine (JVM) running on the target system.
You can use the following command to start all the Hadoop daemons:
./sbin/start-all.sh
You can use the following command to stop all the Hadoop daemons:
./sbin/stop-all.sh
An edge node is a computer that acts as an end-user portal for communicating with other nodes in a Hadoop cluster. An edge node provides an interface between the Hadoop cluster and an outside network. For this reason, it is also referred to as a gateway node or edge communication node. Edge nodes are often used to run administration tools or client applications. They typically do not run any Hadoop services.
NFS, which stands for Network File System, is a widely implemented distributed file system protocol used extensively in network-attached storage (NAS) systems. It is one of the oldest distributed file storage systems and is well-suited to smaller data sets. NAS makes data available over a network but accessible like files on a local machine.
HDFS is a more recent technology. It is designed for handling big data workloads, providing high throughput and high capacity, far beyond the capabilities of an NFS-based system. HDFS also offers integrated data protections that safeguard against node failures. NFS is typically implemented on single systems that do not include the inherent fault tolerance that comes with HDFS. However, NFS-based systems are usually much less complicated to deploy and maintain than HDFS-based systems.
Commodity hardware is a device or component that is widely available, relatively inexpensive and can typically be used interchangeably with other components. Commodity hardware is sometimes referred to as off-the-shelf hardware because of its ready availability and ease of acquisition. Organizations often choose commodity hardware over proprietary hardware because it is cheaper, simpler and faster to acquire, and it is easier to replace all or some of the components in the event of hardware failure. Commodity hardware might include servers, storage systems, network equipment or other components.
MapReduce is a software framework in Hadoop that's used for processing large data sets across a cluster of computers in which each node includes its own storage. MapReduce can process data in parallel on these nodes, making it possible to distribute input data and collate the results. In this way, Hadoop can run jobs split across a massive number of servers. MapReduce also provides its own level of fault tolerance, with each node periodically reporting its status to a primary node. In addition, MapReduce offers native support for writing Java applications, although you can also write MapReduce applications in other programming languages.
A MapReduce operation can be divided into the following two primary phases:
MapReduce operations are sometimes divided into phases other than these two. For example, the Reduce phase might be split into the Shuffle phase and the Reduce phase. In some cases, you might also see a Combiner phase, which is an optional phase used to optimize MapReduce operations.
Feature selection refers to the process of extracting only specific information from a data set. This can reduce the amount of data that needs to be analyzed, while improving the quality of that data used for analysis. Feature selection makes it possible for data scientists to refine the input variables they use to model and analyze the data, leading to more accurate results, while reducing the computational overhead.
Data scientists use sophisticated algorithms for feature selection, which usually fall into one of the following three categories:
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. In fact, an outlier can potentially bias an entire result set. That said, outliers can sometimes contain nuggets of valuable information.
Analysts often use the following two techniques to detect outliers:
These are only two of the core methods used to detect outliers. Other approaches include linear regression models, information theoretic models, high-dimensional outlier detection methods and other approaches.
FSCK, which stands for file system consistency check, is an HDFS filesystem checking utility that generates a summary report about the file system's status. However, the report merely identifies the presence of errors; it does not correct them. The FSCK command can be executed against an entire system or a select subset of files.
Here's your chance to demonstrate your enthusiasm and career ambitions. Of course, your answer will depend on your current level of academic qualifications and certifications, as well as your personal circumstances, which might include family responsibilities and financial considerations. Therefore, respond forthrightly and honestly. Bear in mind that many courses and learning modules are readily available online. Moreover, analytics vendors have established training courses aimed at those seeking to upskill themselves in this domain. You can also inquire about the company's policy on mentoring and coaching.
As mentioned earlier, it's a good rule of thumb to go to interviews with a few prepared questions. But depending on how the conversation has unfolded during the interview, you might 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, you can put your original questions aside.
Also, be aware of how you time your questions, taking your cue from the interviewer. Depending on the circumstances, asking questions during the interview is acceptable, although it's generally more common to hold off on your questions until the end of the interview. That said, you should never hesitate to ask for clarification on a question the interviewer asks.
Remember, the process doesn't end after an interview has ended. 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 haven't 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.
Editor's note: This article was updated in February 2025.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
Elizabeth Davies is a technology writer and content development professional with more than two decades of experience in consulting, strategizing and collaborating with C-suite and other senior information and communication technology experts.
12 Feb 2025