Databricks interview questions (full 2024 list)

For those attempting to ace the data engineer or data scientist interview, this is one of the most intriguing posts. I’ll walk you through a few databricks interview questions and answers in this blog post to help you better your interview preparation while also testing your knowledge. Real-world scenario-based questions, Azure Databricks interview questions for new hires, Azure Databricks interview questions for seasoned professionals, Databricks developer interview questions, and Databricks architect interview questions will all be emphasized in this set of questions.

Databricks interview questions and answers

What do you want candidates to understand about the data team at Databricks before entering the interview process?

Despite the size of the infrastructure that Databricks manages, our engineering team is quite tiny. We manage millions of virtual computers, processing exabytes of data daily while producing terabytes of logs. Our software must gracefully protect our customers from any of the aforementioned issues because at our scale, we frequently observe cloud hardware, network, and operating system flaws. With fewer than 200 engineers, we manage to do everything.

Due to our scale, we are able to adopt or develop the technology that we think will best address each engineering difficulty. On the other hand, because so much of our infrastructure is still developing, there are many initiatives that have concerns that go beyond the purview of a single service. The lines separating ownership and responsibility aren’t always clear because the company is still in its early stages. This indicates that it is simple to make changes and have an impact outside of your primary focus areas, and that you will own a project to a much greater extent than you would elsewhere.

After working at Databricks, what will you have mastered? Scalable solutions can be developed in the Big Data and Machine Learning industry. Most engineers don’t work with applied ML on a daily basis, but we have a thorough understanding of how it is applied for our customers in a variety of industries.

What is the Job in Databricks?

Job is the way to run the task in non-interactive way in the Databricks. It can be used for the ETL purpose or data analytics task. You can trigger the job by using the UI , command line interface or through the API. You can do following with the Job :

• Create/view/delete the job
• You can do Run job immediately.
• You can schedule the job also.
• You can pass the parameters while running the job to make it dynamic.
• You can set the alerts in the job, so that as soon as the job gets starts, success or failed you can receive the notification in the form of email.
• You can set the number of retry for the failed job and the retry interval.
• While creating the job you can add all the dependencies and can define which cluster to be used for executing the job.

What are the components of Databricks?

• Workspace for developers to code collaboratively in real-time securely.
• Managed Clusters to scale up the query speed.
• Spark Engine to manage in-memory data processing
• Delta to overcome the shortcomings in conventional data lake file formats
• ML Flow to overcome challenges in production rising ML lifecycle
• SQL Analytics to develop queries to extract data from data lakes and publish it in dashboards.

What are the languages supported by Databricks?

R, Python, Scala, Standard SQL, and Java. It also supports several language APIs like SparkR or SparkylR, PySpark, Spark SQL, Spark.api.java.

What is the use of auto-scaling in Azure Databricks?

Auto-scaling allows the program to run effectively even under high load. Such a question helps the hiring manager assess your knowledge of auto-scaling in Azure. While answering, briefly define Databricks’s auto-scaling feature and mention its key benefit.

Sample answer: ‘The auto-scaling functionality of Databricks enables users to automatically scale the cluster up or down with their demands. Ensuring users are only using the resources they require helps save time and money.’

What is the difference between data warehouses and Data lakes?

Data Warehouse mostly contains processed structured data required for business analysis and managed in-house with local skills. Its structure cannot be changed so easily.

Data lakes contain all data including raw and old data, all types of data including unstructured, it can be scaled up easily and the data model can be changed quickly. It is maintained by third-party tools preferably in the cloud and it uses parallel processing in crunching the data.

What are the major benefits of Azure Databricks?

Azure Databricks is a market-leading, cloud-based data management tool helpful for processing and manipulating enormous amounts of data and analysing the data using machine learning models. A recruiter may ask such questions to find your interest in Databricks. To convince the interviewer of your technical proficiency, mention a few key benefits and their importance in your answer.

Sample answer: ‘Though Azure Databricks is based on Spark, it supports many other programming languages such as Python, R and SQL. To integrate these with Spark, Databricks converted these languages on the backend through application performance indicators (APIs). This eliminates the users’ requirement to learn any additional programming language for distributed analytics. Azure Databricks is highly adaptable and simple to implement, making distributed analytics much easier to use.

Databricks offers an integrated workspace that supports collaboration through a multi-user environment, enabling a team to develop innovative machine learning and streaming applications on Spark. It also provides monitoring and recovery tools that help recover clusters from failures without manual intervention. With Databricks, we can make our cloud infrastructures secure and fast without major customisation with Spark.’

Is there no on-premises option for Databricks and is it available only in cloud?

Yes. Apache Spark, the base version of Databricks was offered in an on-premises solution and in-house Engineers could maintain the application locally along with the data. Databricks is a cloud-native application and the users will face network issues in accessing the application with data in local servers. Data inconsistency and workflow inefficiencies are the other factors weighed against the on-premises options for Databricks.

Can you run Databricks on private cloud infrastructure?

Such a question may help the interviewer assess your knowledge about the versatility of Databricks. You can also use this question to demonstrate your problem-solving and attention to detail capabilities. In your answer, mention the available cloud server options and also briefly explain how to run it on a private cloud.

Sample answer: ‘Amazon Web Services (AWS) and Azure are the only options available now. Databricks utilises open-source Spark. We can develop our own cluster and run it in a private cloud, but in that case, we miss out on Databricks’ full administration capabilities and features.’

What is the difference between Databricks and Azure Databricks?

Databricks unified Apache Spark’s processing power of data analysis and ML-driven data science/ Engineering techniques in managing the entire data lifecycle from ingestion state up to consumption state.

Azure Databricks combines some of Azure’s capability along with the analytics features of Databricks to offer the best of both worlds to the end-user. It uses Azure’s own data Extraction tool, Data Factory for culling out data from various sources and combines with AI-driven Databricks analytics capability in Transformation and Loading. It also uses MS active directory integration features to gain authentication and other Azure’s and general features of MS to improve productivity.

What is the category of Cloud service offered by Databricks? Is it SaaS or PaaS or IaaS?

The service offered by Databricks belongs to the Software as a service (SaaS) category and the purpose is to exploit the powers of Spark with clusters to manage storage. The users will have to change just the application configurations and start deploying them.

What is the category of Cloud service offered by Azure Databricks? Is it SaaS or PaaS or IaaS?

The service offered by Azure Databricks belongs to the Platform as a service (PaaS) category. It provides an application development platform with capabilities built from Azure and Databricks. The users will have to design and develop the data life cycle and develop applications using the services offered by Azure Databricks.

Compare Azure Databricks and AWS Databricks

Azure Databricks is the well-integrated product of Azure features and Databricks features.
It’s not a mere hosting of Databricks in the Azure platform. MS features like Active directory authentication and integration of many of Azure functionalities make Azure Databricks as a superior product. AWS Databricks is a mere hosting Databricks on AWS cloud.

What purpose does the Databricks file system serve?

The Databricks file system is the process of a decentralized file that provides data durability even when the Azure Databricks node is removed.

How do you handle Databricks code while working in a team using TFS or Git?

To begin, TFS is not supported. Git and distributed Git repository systems are your only options. While it would be great to attach Databricks to your Git directory of notebooks, Databricks functions similarly to another clone of your project. You begin by creating a notebook, committing it to version control, and then updating it.

Can Databricks be run on private cloud infrastructure, or must it be run on a public cloud such as AWS or Azure?

That is not the case. At this time, your only alternatives are AWS and Azure. However, Databricks runs open-source Spark. You could create your own cluster and operate it in a private cloud, but you’d be missing out on Databricks’ extensive capabilities and administration.

 What is a Databricks secret?

A secret is a key-value combination that contains secret content; it is composed of a unique key name contained within a secret context. Each scope is limited to 1000 secrets. The secret value cannot exceed 128 KB in size.

What is the use of Databricks filesystem?

Ans. The Databricks filesystem is used to store data in Databricks. It’s a distributed file system that is designed for big data workloads. The Databricks filesystem is compatible with the Hadoop Distributed File System (HDFS).

What languages can be used in Azure Databricks?

Ans. You can use any language that is supported by the Apache Spark platform, including Python, Scala, and R. In addition, you can use SQL with Azure Databricks.

What is the delta table in databricks?

Ans. A delta table is a type of table that stores data in the Databricks Delta format. Delta tables are optimized for fast reads and writes, and they support ACID transactions.

What is Databricks Runtime?

Ans. Databricks Runtime is a software platform that runs on top of Apache Spark. It includes libraries, APIs, and tools that make it easy to build and run Spark applications.

What are the most common mistakes you see during interviews?

Now that we’ve covered what we look for and how to prepare for interviews, there are a few things you should consciously try not to do during an engineering job interview.

The main one is lacking passion or interest in the role. Remember, you are interviewing the company as well and it’s important you show that you are invested in making a match. Having low enthusiasm, not being familiar with the Databricks product, not asking any questions and in general relying on the interviewer to drive the entire conversation are all signs you aren’t interested. Just as you want an interview process that challenges you and dives into your skills and interests, we like a candidate that asks us tough questions and takes the time to get to know us.

For technical interviews, if a candidate is pursuing a solution that won’t work, we try to help them realize it before spending a lot of time on implementation. If the interviewer is asking questions, chances are they are trying to hint you towards a different path. Rather than staying fixed on a single track solution, take a minute to step back and reconsider your approach with new hints or questions. Remember that your interviewer has probably asked the same question dozens of times and seen a range of approaches. They also want to see how you’d respond in a real-world environment, where you’d be working with a team that offers help in a similar way.

For interviews focused on work history and soft skills, have specific examples. It’s ok to start with broad generalization, but tell a story about how specific examples in your past work history answer the question. When talking about your work experience, try to (1) clearly define the problem, (2) your solution, (3) the outcome and (4) any reflections on improvements. A good way to provide a well thought-out answer is by using the STAR Interview Response Technique.

FAQ

Is Databricks interview hard?

How do I prepare for a Databrick interview?

1. Find coding questions online and practice solving them completely. …
2. Review computer science fundamentals. …
3. Do mock interviews.

How many rounds of interview are in Databricks?

What is azure Databricks interview questions?

1. Do Compressed Data Sources Like . csv. …
2. Should You Clean Up DataFrames, Which Are Not in Use for a Long Time? …
3. Do You Select All Columns of a CSV File When Using Schema With Spark . …
4. Can You Use Spark for Streaming Data? …
5. Does Text Processing Support All Languages?

What is the interview process like at Databricks?

Common stages of the interview process at Databricks according to 336 Glassdoor interviews include:

Phone Interview: 25.54%

One on One Interview: 20.66%

Presentation: 15.55%

Drug Test: 11.46%

Group Panel Interview: 9.31%

Background Check: 8.97%

IQ Intelligence Test: 3.97%

Other: 2.84%

Personality Test: 0.91%

Skills Test: 0.79%