Top Databricks Interview Questions and Answers for 2026

The Databricks Interview Process

Before diving into individual questions, it helps to know what the interview process typically looks like. Based on my experience and current reports from candidates in 2026, a typical Databricks interview for engineering and data roles runs five to six stages over four to seven weeks.

The process will, of course, vary by company, but you should be prepared for the following:

The questions below map to the technical screen and onsite rounds. Behavioral preparation is outside the scope of this guide, but the Databricks Certifications guide gives a good sense of the platform depth interviewers expect.

Basic Databricks Interview Questions

Now, at a basic user level, interview questions will focus on foundational knowledge of Databricks, including basic tasks like deploying notebooks and using the essential tools available within the platform. You are likely to encounter these questions if you have had limited experience with Databricks or if the interviewer isn’t certain of your skill level. 

Below are some of the key topics you are likely to be asked about. Read also our Databricks Tutorial: 7 Must-Know Concepts as an additional resource to prepare.

• High-Level Overview of Databricks: You should be able to describe what Databricks is and also how it fits into a modern data platform. 
• Core Feature and Users: You should know about collaborative workspaces, notebooks, the optimized Spark engine, and the ability to handle both batch and streaming data.
• Simple Use Cases: You should provide some high-level examples of how customers use Databricks, including some insight into basic architecture.

Also, if the idea of streaming data is new to you, then I'd recommend taking a look at our Streaming Concepts course to boost your knowledge in this area. 

1. What is Databricks, and what are its key features? 

Databricks is a data analytics platform known for its collaborative notebooks, its Spark engine, and its data lakes, such as Delta Lake which has ACID transactions. Databricks also, of course, integrates with various data sources and BI tools and offers good security features.

2. Explain the core architecture of Databricks.

The core architecture breaks into five parts.

• The Databricks Runtime bundles Spark and other components that run on a cluster.
• Clusters are the compute resources that execute notebooks and jobs.
• Notebooks mix code, visualizations, and text in a single interactive document.
• The workspace organizes notebooks, libraries, and experiments.
• The Databricks File System (DBFS) provides a distributed file system attached to those clusters.

3. How do you create and run a notebook in Databricks? 

First, go to the Databricks workspace where you want to create your notebook. Click on “Create” and choose “Notebook.” Give your notebook a name and select the default language, such as Python, Scala, SQL, or R. Next, attach it to a cluster. Then, to run your notebook, simply write or paste your code into a cell and then click the "Run" button.

Intermediate Databricks Interview Questions

These questions will come once your interviewer has established that you have some basic knowledge of Databricks. They are usually a bit more technical and will test your understanding of specific parts of the platform and their configurations. At an intermediate level, you’ll need to demonstrate your ability to manage resources, configure clusters, and implement data processing workflows. 

This will build upon your basic knowledge of the platform and understanding of the following parts of the platform: 

• Managing Clusters: You should understand how to set up and manage clusters. This includes configuring clusters, selecting instance types, setting up auto scaling, and managing permissions. 
• Spark on Databricks: You should be proficient in using Apache Spark within Databricks. This includes working with DataFrames, Spark SQL, and Spark MLlib for machine learning. You can also deepen your PySpark skills with our PySpark Interview Questions guide. 
• Resource Monitoring: You should know how to use the Databricks UI and Spark UI to track resource usage and job performance, and also to identify bottlenecks. 

If working with large datasets and distributed computing is new to you, then I'd recommend taking a look at the following skill track: Big Data with PySpark, which introduces PySpark, an interface for Apache Spark in Python 

4. How do you set up and manage clusters? 

To set up a cluster, start by heading over to the Databricks workspace and clicking on "Clusters." Then, hit the "Create Cluster" button. You'll need to configure your cluster by choosing the cluster mode, instance types, and the Databricks Runtime version, among other settings. Once you're done with that, simply click "Create Cluster". Then, to manage clusters, you can monitor resource usage, configure autoscaling, install necessary libraries, and manage permissions through the Clusters UI or using the Databricks REST API.

5. Explain how Spark is used in Databricks.

Databricks uses Apache Spark as its main engine. In Databricks, Spark handles large-scale data processing with RDDs and DataFrames, runs machine learning models through MLlib, manages stream processing with Spark Structured Streaming, and executes SQL-based queries with Spark SQL. 

6. What are data pipelines, and how do you create them? 

Data pipelines are basically a series of steps to process data. To set up a data pipeline in Databricks, you start by writing ETL scripts in Databricks notebooks. Then, you can manage and automate these workflows using Databricks Jobs. For reliable and scalable storage, Delta Lake is a good choice — read our Delta Lake introduction if you need a refresher. Databricks also lets you connect with various data sources and destinations using built-in connectors.

7. How do you monitor and manage resources in Databricks? 

Databricks gives you three main options for tracking and managing resources. First, you can use the Databricks UI, which lets you track cluster performance, job execution, and how resources are being used. Then there's the Spark UI, which provides job execution details, including stages and tasks. If you prefer automation, the Databricks REST API offers a way to programmatically manage clusters and jobs.

8. Describe the data storage options available in Databricks. 

Databricks offers several ways to store data. First, there's the Databricks File System for storing and managing files. Then, there's Delta Lake, an open-source storage layer that adds ACID transactions to Apache Spark, making it more reliable. Databricks also integrates with cloud storage services like AWS S3, Azure Blob Storage, and Google Cloud Storage. Plus, you can connect to a range of external databases, both relational and NoSQL, using JDBC.

Advanced Databricks Interview Questions

Advanced users of Databricks are expected to perform tasks such as performance optimization, creating advanced workflows, and implementing complex analytics and machine learning models. Typically, you will only be asked advanced questions if you are applying for a senior data position or a role with a strong DevOps component. If you are interested in interviewing for advanced positions and need to build out that side of your skill set, our DevOps Concepts course is a great resource. Additionally, check our Data Architect Interview Questions and our Top 20 Spark Interview Questions and our comparison of Databricks vs Snowflake article.

This will build upon your basic and intermediate knowledge of the platform as well as practical experience. 

• Performance Optimization: Advanced users need to focus on optimizing performance. This includes tuning Spark configurations, caching data, partitioning data appropriately, and optimizing joins and shuffles. 
• Machine Learning: Implementing machine learning models involves training models using TensorFlow or PyTorch. You should be proficient in using MLflow for experiment tracking, model management, and deployment, ensuring your models are reproducible and scalable.
• CI/CD Pipelines: Building CI/CD pipelines involves integrating Databricks with version control, automated testing, and deployment tools. You should know how to use Databricks CLI or REST API for automation and ensure continuous integration and delivery of your Databricks applications.

If working with machine learning and AI in Databricks is new to you, then I'd recommend taking a look at the following tutorial to boost your knowledge in this area: A Comprehensive Guide to Databricks Lakehouse AI For Data Scientists. I would also look seriously at our Introduction to TensorFlow in Python and Intermediate Deep Learning with PyTorch courses to complement your other work in Databricks.

9. What strategies do you use for performance optimization? 

For performance optimization, I rely on Spark SQL for efficient data processing. I also make sure to cache data appropriately to avoid redundancy. I remember to tune Spark configurations, like adjusting executor memory and shuffle partitions. I pay special attention to optimizing joins and shuffles by managing the data partitioning. I would also say that using Delta Lake helps with storage and retrieval while supporting ACID transactions.

10. How can you implement CI/CD pipelines in Databricks? 

Setting up CI/CD pipelines in Databricks involves a few steps. First, you can use version control systems like Git to manage your code. Then, you can automate your tests with Databricks Jobs and schedule them to run regularly. It’s also important to integrate with tools such as Azure DevOps or GitHub Actions to automate the deployment pipeline. Lastly, you can use the Databricks CLI or REST API to deploy and manage jobs and clusters.

11. Explain how to handle complex analytics in Databricks.

Spark SQL and DataFrames handle advanced queries and transformations. For machine learning and statistical analysis, the built-in MLlib library covers most use cases. Third-party analytics tools connect via JDBC or ODBC. For interactive visualization, Databricks notebooks support Matplotlib, Seaborn, and Plotly.

12. How do you deploy machine learning models? 

Deploying machine learning models in Databricks follows a clear pattern. First, you train your model using libraries like TensorFlow, PyTorch, or Scikit-Learn. Then, you use MLflow to keep track of your experiments, manage your models, and make sure everything’s reproducible. To get your model up and running, you deploy it as a REST API using MLflow’s features. Lastly, you can set up Databricks Jobs to handle model retraining and evaluation on a schedule.

Databricks Interview Questions for Data Engineer Roles

Data Engineers are responsible for designing and building data, analytics, and AI systems that handle large volumes reliably, managing data pipelines, and ensuring overall data quality. For data engineers, the focus is on designing and building data systems, managing pipelines, and ensuring data quality. 

When applying for Data Engineer positions that focus heavily on Databricks, you should have a good understanding of the following topics: 

• Data  Pipeline Architecture: Designing robust data pipeline architectures involves understanding how to extract, transform, and load (ETL) data efficiently. You should be able to design pipelines that handle growing data volumes, recover from failures, and stay maintainable using Databricks features like Delta Lake.
• Real-Time Processing: Handling real-time data processing requires using Spark Structured Streaming to ingest and process data in near real-time. You should be able to design streaming applications that are fault-tolerant and able to process events within seconds of ingestion.
• Data Security: Ensuring data security involves implementing encryption, access controls, and auditing mechanisms. You should be familiar with Databricks' integration with cloud provider security features and best practices for securing data at rest and in transit.

13. How do you design data pipelines? 

Designing a data pipeline in Databricks usually starts with pulling data from different sources using Databricks connectors and APIs. Then, you transform the data with Spark transformations and DataFrame operations. After that, you load the data into your target storage systems, such as Delta Lake or external databases. To keep things running, you automate the whole process with Databricks Jobs and workflows. Plus, you monitor and manage data quality using the built-in tools and custom validations.

14. What are the best practices for ETL processes in Databricks? 

In my experience, these practices matter most for ETL in Databricks. Start by using Delta Lake for storage, as it offers reliability and scalability with ACID transactions. Writing modular and reusable code in Databricks notebooks is also a smart move. For scheduling and managing your ETL jobs, Databricks Jobs is a handy tool. Keep an eye on your ETL processes with Spark UI and other monitoring tools, and don't forget to ensure data quality with validation checks and error handling.

15. How do you handle real-time data processing? 

In the past, I've managed real-time data processing in Databricks by using Spark Structured Streaming to handle data as it comes in. I’d set up integrations with streaming sources like Kafka, Event Hubs, or Kinesis. For real-time transformations and aggregations, I wrote streaming queries. Delta Lake was key for handling streaming data efficiently, with quick read and write times. To keep everything running smoothly, I then monitored and managed the streaming jobs using Databricks Jobs and Spark UI.

16. How do you ensure data security? 

To keep data secure, I use role-based access controls to manage who has access to what. Data is encrypted both at rest and while it's being transferred, thanks to Databricks’ encryption at rest and in transit. I then also set up network security measures like VPC/VNet and ensure that access is tightly controlled there. To keep an eye on things, I’ve previously used Databricks audit logs to monitor access and usage. Lastly, I make sure everything aligns with data governance policies by using Unity Catalog — for a deeper look at this tool, read our Databricks Unity Catalog guide.

Databricks Interview Questions for Software Engineer Roles

Software engineers working with Databricks need to develop and deploy applications and integrate them with Databricks services. 

When applying for this type of position, you should have a strong understanding of the following topics:

• Application Development: Developing applications on Databricks involves writing code in notebooks or external IDEs, using Databricks Connect for local development, and deploying applications using Databricks Jobs. 
• Data Integration: Integrating Databricks with other data sources and applications involves using APIs and connectors. You should be proficient in using REST APIs, JDBC/ODBC connectors, and other integration tools to connect Databricks with external systems.
• Debugging: Debugging Databricks applications involves using the Spark UI, checking logs, and interactive testing in notebooks. Implementing detailed logging and monitoring helps identify and resolve issues effectively, ensuring your applications run smoothly and reliably.

If you're new to developing applications and want to enhance your skills, then I'd recommend taking a look at our Complete Databricks Dolly Tutorial for Building Applications, which guides you through the process of building an application using Dolly. 

17. How do you integrate Databricks with other data sources using APIs? 

To connect Databricks with other data sources using APIs, start by using the Databricks REST API to access Databricks resources programmatically. You can then also connect to external databases through JDBC or ODBC connectors. For more comprehensive data orchestration and integration, tools like Azure Data Factory or AWS Glue are really useful. You can create custom data ingestion and integration workflows using Python, Scala, or Java.

18. How do you develop and deploy applications on Databricks? 

Here's how I usually go about deploying applications: First, I write the application code, either directly in Databricks notebooks or in an external IDE. For local development and testing, I use Databricks Connect. Once the code is ready, I package and deploy it using Databricks Jobs. To automate the deployment process, I rely on the REST API or Databricks CLI. Finally, I keep an eye on the application’s performance and troubleshoot any issues using Spark UI and logs.

19. What are the best practices for performance tuning? 

When it comes to performance tuning in Databricks, I would advise that you make sure you optimize your Spark configurations according to what your workload needs. Using DataFrames and Spark SQL can also make data processing a lot more efficient. Another tip is to cache data that you use frequently. This helps cut down on computation time. It’s also important to partition your data to evenly distribute the load across your clusters. Keep an eye on job performance and look out for bottlenecks.

20. How do you debug issues in Databricks applications? 

I start with the Spark UI to find which stages or tasks are failing. Databricks logs give error messages and stack traces for anything the UI doesn't surface. I also use notebook cells for interactive spot-testing, and I make sure application code has enough logging to trace failures at runtime.

Advanced Databricks Interview Questions for 2026

The Databricks platform has evolved significantly since 2024. Three topics now appear consistently in advanced interviews:

• Unity Catalog for governance
• The Medallion Architecture for data organization
• Delta Live Tables for declarative pipeline management.

If you are interviewing for a senior role in 2026, expect at least one question from this section.

21. What is Unity Catalog, and why does it matter in a modern Databricks environment?

Unity Catalog is Databricks’ centralized governance layer for all data and AI assets. It replaces the legacy Hive Metastore and provides fine-grained access controls down to the row and column level, cross-workspace data sharing, automated data lineage, and a unified audit log.

In practice, Unity Catalog lets a data platform team manage access policies for hundreds of workspaces from a single interface, which is something the old per-workspace Hive Metastore simply could not do.

22. Explain the Medallion Architecture and when you would use it.

The Medallion Architecture is a data organization pattern that layers Delta Lake tables into three zones:

• Bronze (raw ingested data, unchanged)
• Silver (cleaned and conformed data)
• Gold (aggregated, business-ready data)

You use it when you need a reliable audit trail — Bronze preserves the source record exactly as it arrived. Silver handles deduplication, schema enforcement, and joins. Gold serves BI tools and ML features. Most production Databricks environments I have worked in use this pattern because it makes data quality issues traceable and re-processable without starting from scratch.

23. What are Delta Live Tables (DLT), and how do they differ from standard Databricks Jobs?

Delta Live Tables is a declarative framework for building data pipelines in Databricks. Instead of writing imperative Spark code that reads from table A and writes to table B, you define what each table should contain using SQL or Python, and DLT figures out the execution order, handles dependencies, and manages retries automatically. The key difference from standard Jobs is that DLT provides built-in data quality expectations (using the EXPECT constraint), automatic pipeline lineage, and simplified error handling. I find DLT particularly useful for Medallion-style pipelines where the Bronze-to-Silver-to-Gold transformations benefit from declarative dependency management.

24. What is the Photon engine, and when does it improve performance?

Photon is Databricks’ native vectorized query engine written in C++. It runs as part of the Databricks Runtime and accelerates SQL and DataFrame workloads by processing data in columnar batches rather than row by row. Photon is most effective on scan-heavy, aggregation-heavy, and join-heavy queries on large Parquet or Delta tables — the kinds of workloads typical in BI dashboards and feature engineering. It does not improve workloads that are Python-heavy or that rely on custom UDFs, since those still execute on the JVM.

25. Why would you choose Databricks over Snowflake (or vice versa)?

Databricks leads on open-source compute (Spark, Delta, MLflow), AI and ML workloads, and the Lakehouse model with structured and unstructured data. Snowflake leads on SQL-first analytics, multi-cloud data sharing, and simplicity for BI teams.

Interviewers use this to gauge whether candidates understand the strategic positioning of the platform, not just its mechanics. For a detailed comparison, see our Databricks vs Snowflake breakdown.

Final thoughts 

I hope you found this interview guide helpful as you prepare for your Databricks interview. Of course, there is no substitute for solid preparation and practice, which is why I advocate taking both DataCamp’s Databricks Concepts and Introduction to Databricks courses, which are sure to give you the ability to understand and talk about Databricks in a way that will impress an interviewer. I also recommend familiarizing yourself with the Databricks documentation. Reading documentation is always a good idea.

Finally, have a listen to the DataFramed podcast episode on the way to your interview, and learn from the CTO of Databricks How Databricks is Transforming Data Warehousing and AI. It’s important to hear from the industry leaders and stay current because things are changing fast.

Good luck!