Hadoop is a popular framework that stores large amounts of data. Interviewers and hiring managers usually ask Hadoop-related questions when interviewing for roles in data management and analytics. Understanding the basics of Hadoop and preparing common questions related to this topic can help you prepare better and feel more confident during an interview. In this article, Pritish Kumar Halder reviews common Hadoop interview questions and offers sample answers to help prepare for your next interview.
11 common Hadoop interview questions
The complexity of Hadoop interview questions can vary based on the position, experience level and other role requirements. Following are some frequently asked questions and example answers related to Hadoop:
1. What is big data?
Big data is a large collection of data that is complex and challenging to process. Your ability to explain big data can show the interviewer that you understand the concept thoroughly and the various challenges of processing big data sets. In your response, you can also include examples of when you worked with big data in previous positions and projects.
Example answer: “Big data is a term used to describe a large volume of data that a business deals with on a regular basis. Analysis of big data can help extract significant value and information, which can be beneficial for the company’s growth. It can help organisations make strategic and informed business decisions. I worked as a developer in my previous organisation and was responsible for programming Hadoop applications. My duties involved analysing large data sets to help find actionable insights.”
2. What are the five V’s of big data?
This is a common question that both experienced and novice data professionals may encounter during interviews. Interviewers ask this question to evaluate the candidate’s ability to explain the fundamental aspects of big data. In your response, you can explain all the five terms that constitute the five V’s without using overly technical language and terms.
Example answer: “The first V stands for volume. Companies produce a huge volume of data regularly through cell phones, social media, credit cards and various other sources. The second V is velocity. Velocity describes the rate at which the volume of big data grows. With online media, the amount of big data can continue to increase exponentially on a day-to-day basis. The third V, Variety, stands for the different types of data. This may include structured data like names, addresses and phone numbers and various other forms of unstructured data.
The next V is veracity which denotes uncertain and unstable data sets. An example of this is when a GPS tracker goes off course. With lost or inaccurate signals, the data provided is uncertain until the signal is restored. The fifth V indicates value, an important aspect from a business perspective. The value represents various insights and patterns that arise because of the analysis of big data.”
3. How many input formats exist in Hadoop?
This is a question that professionals with a few years of experience may encounter. Explaining the three input formats is an effective answer to this question.
Example answer: “The first input format is the default text input format which reads lines of text files. The second input format is file input which reads files in a specified sequence. The key-value input format is similar to the text input format, breaking each line into key-value inputs. In this format, the first value occurring before delimiter is the key, and the rest is value.”
4. Explain YARN.
The interviewer may ask you what YARN stands for to test your level of knowledge in Hadoop. Ensure that you explain this concept in the domain of Hadoop.
Example answer: “YARN stands for Yet Another Resource Negotiator and enables job scheduling and resource management in Hadoop. Earlier, Hadoop version 1.0 used MapReduce for both processing and management of resources. YARN as the processing layer helps reduce the burden on MapReduce, leading to increased efficiency.”
5. Who uses Hadoop?
Interviewers can ask this question to check your experience in using Hadoop. It can be a good way to assess a candidate’s practical experience with real-world Hadoop applications. You can read about the various companies and businesses that use the platform. Ensure you refer to reputable and verifiable sources and state them during the interview if required.
Example answer: “Several Fortune 500 companies use Hadoop to handle distributed processing for big data. Some top industries that use big data include banking and finance, manufacturing, retail and healthcare.”
6. What are the main features of Hadoop?
The hiring manager can ask this question to assess your fundamental understanding of Hadoop and big data. To answer this question, explain some of the most significant benefits of using Hadoop over other frameworks.
Example answer: “Hadoop is an open-source framework, meaning you can easily modify it to meet customised business requirements. It helps in distributed processing and is fault-tolerant and reliable. It is also economical, scalable and easy to use. The framework works on the principle of data locality which involves moving the computation to the node where the data resides, to reduce network congestion and increase throughput.”
7. Can you give me an example of a scheduler in Hadoop?
This question is an opportunity to display your knowledge of the infrastructure of Hadoop. You can list down the three types of schedulers: FIFO, COSHH and fair scheduling and explain each one briefly.
Example answer: “There are three types of schedulers. The COSHH scheduler performs scheduling based on the cluster, its workload and heterogeneity. In FIFO, the scheduler lines up jobs based on a first-come, first-serve basis. This is the default scheduler in Hadoop. Fair scheduling assigns resources to applications in a way that over time, all apps get an equal share of resources on average.”
8. Which operating systems can run Hadoop?
This can be a trick question in a Hadoop interview. Pay attention to the interviewer, and determine if they pronounce systems with an ‘s’ at the end or if they just say the word system. In your response, you can mention the primary system for Hadoop and other alternative operating systems if necessary.
Example answer: “Linux is the primary system for Hadoop deployment, but it can also run on Windows systems.”
9. What does a JobTracker do?
Interviewers may ask this question to evaluate your technical expertise and knowledge in the Hadoop domain. In your answer, briefly list down the various functions of the JobTracker.
Example answer: “JobTracker has several functions. Besides managing resources and keeping track of the ones available for a particular task, it also works hand-in-hand to decide which resources are best for the task. The JobTracker is responsible for tracking each task and submitting the overall work to the client. It identifies the data location through communication with the NameNode and tracks MapReduce workloads. Here, it uses both the local and slave modes.”
10. Briefly explain HDFS in Hadoop.
Interviewers can ask this question to test your knowledge of the Hadoop architecture. In your response, you can explain the main modules of the HDFS, its two daemons and its functions.
Example answer: “HDFS is Hadoop’s storage layer. The acronym HDFS stands for Hadoop Distributed File System. HDFS files consist of data blocks that measure 128 MB by default. HDFS follows the master-slave architecture and has two daemons-NameNode and DataNode.
The NameNode saves filesystem metadata such as block locations, directions, file names and permissions. NameNode assigns tasks to the slave nodes and manages them. DataNode is the slave daemon that saves the business data and is responsible for block creation, replication and deletion. It also serves read-write requests from the client.”
11. Explain the three modes of running Hadoop.
Interviewers ask this question to check your understanding of Hadoop deployment. To answer this question, briefly list down the three modes and explain each of them concisely.
Example answer: “Hadoop can run on three modes, which are the standalone or local mode, pseudo-distributed mode and fully distributed mode. The standalone mode is the default mode for Hadoop. All processes in standalone mode run on one JVM (Java Virtual Machine). Pseudo-distributed mode uses custom configuration. Here, daemons run on individual Java processes. This mode finds is usually meant for debugging and testing purposes. The fully-distributed mode uses multiple nodes out of which few run the master daemons while others run slave daemons. This works as the production mode of Hadoop.”