Software Engineer working on a project to develop skills

As the data management and analysis landscape continues to evolve, Apache Hadoop remains a cornerstone technology drastically changing the way organizations handle massive amounts of data. If you’re applying for a job as a Hadoop developer, you may face complex interview questions. Let’s dig into a Hadoop developer interview might go!

What is Hadoop?

In simplest terms, Apache Hadoop is an open-source platform that allows organizations, developers, and data scientists to crunch data on a distributed network of computers. It includes frameworks for facilitating distributed data storage and “Big Data” processing. The Hadoop Distributed File System (HDFS) stores data in big blocks across a network of devices/nodes, while MapReduce processes that data in the nodes.

This distributed storage and parallel processing is fast and efficient, in theory. Hadoop enjoyed an enormous burst of popularity more than 15 years ago, when lower-powered hardware and processors necessitated the use of a network to process Big Data loads. Today, many organizations continue to rely on it for their biggest data projects.

How should I prepare for a Hadoop developer job interview?

Hadoop is used for a wide array of specialized data-analysis functions within an organization, including (but definitely not limited to) web traffic analysis, machine learning modeling, image processing, archiving data, and much more.

Before entering any interview for a Hadoop developer job, you’ll need to know the organization’s specialized needs. What do they use Hadoop for? What will you need to understand about their underlying business needs and challenges? In addition to Hadoop-centric technical skills, what other industry-specific information might you need to know?

Sometimes an employer will provide much of that information for you ahead of the interview. You can also do your own research online. If you’re absolutely stumped, however, don’t be afraid to send an email (or three) to the hiring manager or recruiter, asking about how Hadoop is used in the context of the organization’s strategy.

How I succeed in a Hadoop developer job interview?

First and foremost, companies will try to determine whether you have the technical mastery of Hadoop necessary for the job. Praveen Pampati, director of data engineering at ActiveCampaign, says the types of questions aspiring developers can anticipate will cover a broad range of topics, but the interview will most likely start with questions directed at what distribution of Hadoop they're familiar with.

"I would want to know if they are working with MapR, Hortonworks Data Platform (HDP), Cloudera CDH, Apache, or another distribution," he explains. "There are different distributions available, and they are best suited for different use cases."

To that end, possible questions you might face include:

  • Why is the data distributed?
  • What’s the difference between Hadoop’s three modes (pseudo-distributed, fully distributed, and standalone)?
  • For the purposes of this organization, is it best to go with open source and build upon it?
  • What are the advantages of choosing a managed servics like MapR, or a full package system like Hortonworks?
  • Break down the difference between high availability and federation.

Owen O'Malley, a Hadoop project management committee member at Apache, emphasizes how interviewers will want to glean some knowledge of the developer's expertise and grasp of Hadoop Distributed File System (HDFS), the distributed file system that stores data across multiple machines in a cluster.

"You want to make sure they understand when to use which tools, and the difference between streaming applications and batch applications," he says.

What are other important Hadoop skills to know?

Interviewers will try to determine whether you can manage day-to-day Hadoop tasks. For example, can you ensure batch applications (which run periodically; once an hour or day, for example) run smoothly? Do you know how to set up a system to reduce the amount of cost-intensive processing?

As a result, you’ll face technical questions such as:

  • If you have a bunch of queries that look at your database for which users have downloaded certain applications, how would you lay it out?
  • When should SQL be used, and when is Trino better to use?

In addition, you’ll inevitably face questions about your previous work experience with Hadoop, so come prepared with stories about challenges you’ve solved and projects you’ve guided to success. O’Malley suggests preparing a few narratives that break down the systems you’ve designed (within reason; you can’t reveal a previous employer’s proprietary information) and innovative ways you’ve overcome obstacles. “You're looking for that distributed systems expertise,” he says.

Those “work experience” questions might include:

  • How do you deal with failure or overload?
  • How do you ensure a client isn't going to do a denial of service attack against a service?

Other questions might focus on how you’ve handled scaling, since Hadoop's architecture is inherently designed to scale and handle large volumes of data by distributing the workload across multiple nodes in a cluster. “You can expect questions where you're given a problem and are asked how to process the workload and what your approach would be and why,” he says.

How do I pass a technical interview?

Depending on the specific Hadoop developer role, you may face a variety of highly technical questions. Those questions will focus on various Hadoop frameworks and tools, including Hive and Pig, as well as MapReduce. On a most fundamental level, however, many interviewers are trying to see how you’d effectively architect a Hadoop system that can seamlessly store and process data, and technical questions may focus on how you’d achieve that goal.

For example:

  • How should they aggregate data?
  • Where would you store it?
  • How many machines would you need to store it?
  • How many machines would you need to process it?
  • How should data evolve over time?

“Getting ballparks for that kind of stuff makes a huge difference and is good to see if they can model roughly what's going to happen when they run a job,” O'Malley says. “What you don't want to do is that someone just tries it and blows something up.”

What other questions are typically asked during a Hadoop developer interview?

As with many other tech roles, “soft skills” such as empathy and communication are absolutely key for any Hadoop developer role. Organizations need to know that you’ll work effectively with stakeholders throughout the organization, including data scientists and software engineers. For the job interview, you’ll want to come prepared with stories about how you’ve used your soft skills to help teams succeed with Hadoop distribution and analysis.

When it comes to soft skills, Pampati likes to ask use case-driven questions to evaluate if somebody is a team player. "For example, let's say you have a tight deadline to complete a task today, but you also have a colleague or team member struggling with another task, which you think you could solve pretty easily," he says. "How would you respond to those situations?"

Here are some other sample questions you might face:

  • What actions could you take without affecting your current work?
  • How do you help your team members to succeed?
  • How do you bring collective success to the team in those type of scenarios?

Because elements of the Hadoop ecosystem can interact with other systems in ways that are potentially harmful if something goes wrong, communication skills are an intensely vital element of the job.

“It's about communicating what you're doing because you're working on a team,” O'Malley says. “If the best engineers in the world can't communicate what they're doing, they're going to cost you more trouble than they're worth.”