Databricks Interview Questions – Level 1: Top 10 Questions and Answers
Welcome to Level 1 of our Databricks Interview Questions series! This series is designed to take you from the fundamentals of Databricks all the way up to advanced, real-world scenarios that hiring managers actually ask about. Whether you are a fresher trying to break into Data Engineering or an experienced SQL/ETL professional making the switch to the Databricks Lakehouse Platform, this series will build your interview confidence one level at a time.
In this first video of the series, we cover the Databricks interview questions that almost every interviewer starts with — the core concepts around what Databricks is, how it relates to Apache Spark, its architecture, and the everyday building blocks like workspaces, compute, and notebooks. Getting these fundamentals rock solid is essential before you move on to the more scenario-based and hands-on rounds in Level 2 and beyond.
Below, we’ve broken down all 10 questions from the video, along with the exact answers explained on screen, complete with the reference slides. Let’s get started with this set of Databricks interview questions and answers.
1: What is Databricks?
Databricks is a cloud-based data and AI platform built around Apache Spark. It provides a unified environment for data engineering, data warehousing, streaming, analytics, machine learning, and AI workloads.
Databricks follows the lakehouse architecture, combining the scalability of a data lake with the management and performance features of a data warehouse. Delta Lake provides reliable storage, while Unity Catalog provides centralized governance.
2: Why Do Companies Use Databricks?
- Processes very large datasets using distributed computing
- Supports batch and streaming workloads
- Supports Python, SQL, Scala, and R
These reasons come up frequently in Databricks interview questions, since interviewers want to know that you understand the business value, not just the technology.
3: Explain Databricks Lakehouse Architecture
Data lakes: Low-cost, flexible storage for structured and unstructured data. Data warehouses: ACID transactions, governance, SQL analytics, and reliability.
A typical Databricks lakehouse contains:
- Cloud object storage such as ADLS, Amazon S3, or Google Cloud Storage
- Delta Lake as the reliable table-storage layer
- Apache Spark and Photon as processing engines
4: What Are the Main Components of Databricks?
- Workspace (Notebooks, Pipelines/SDP, Scripts, Lakeflow Jobs)
- Compute
- SQL warehouses
- Delta Lake
- Unity Catalog
- Databricks SQL
- Git folders
- Model Serving
- Dashboards
- Secrets and access controls
5: What Is the Difference Between Databricks and Apache Spark?
Spark is the processing engine, while Databricks is the complete platform surrounding that engine.
Apache Spark is an open-source distributed data-processing engine. Databricks is a managed platform built around Spark that provides additional capabilities such as:
- Managed compute
- Collaborative notebooks
- Delta Lake
- Unity Catalog
- Workflow orchestration
- Databricks SQL
- Security, and more
This is one of the most common Databricks interview questions for candidates coming from a pure open-source Spark background.
6: What Is a Databricks Workspace?
A Databricks workspace is a collaborative environment where users create and manage:
- Notebooks
- SQL queries
- Dashboards
- Jobs
- Pipelines
- Git folders
- Experiments
- Models
- Compute resources
7: What Is Compute in Databricks?
Compute refers to the processing resources used to execute notebooks, Spark jobs, SQL queries, and pipelines. Compute usually contains:
- A driver
- One or more workers
- Databricks Runtime
- Spark
- Optional Photon acceleration
- Required libraries and configurations
Compute can be all-purpose, jobs-based, SQL warehouse, or serverless compute.
8: What Is a Databricks Notebook?
A Databricks notebook is an interactive document that combines:
- Executable code
- SQL
- Markdown
- Visualizations
- Results
Notebooks are commonly used for development, testing, analysis, and demonstrations — which is why interviewers love asking practical Databricks interview questions around notebook usage and workflow.
9: What Are the Types of Compute in Databricks?
- All-purpose compute: Used for interactive development, exploration, and notebooks
- Jobs compute: Created to run an automated job and terminated after completion
- SQL warehouse: Optimized for SQL queries, dashboards, and BI tools
10: Which Programming Languages Does Databricks Support?
Databricks primarily supports:
- Python
- SQL
- Scala
- R
It also supports Java through Spark applications and libraries. Python and SQL are the most commonly used languages in Databricks data-engineering projects.
Wrapping Up Level 1
That completes the first set of Databricks interview questions in this series! Mastering these fundamentals — what Databricks is, how it differs from Apache Spark, its lakehouse architecture, and its core building blocks like compute, workspaces, and notebooks — gives you a strong foundation before moving into scenario-based and advanced-level rounds.
Stay tuned for the next video in the Databricks interview questions series, where we’ll go deeper into Delta Lake, Unity Catalog, and real-time data engineering scenarios.
For structured, hands-on training on Databricks, Azure Data Engineering, and more, reach out to SQL School at www.sqlschool.com or call +91 9951440801 for trainings and real-time projects.
