Top 10 Databricks Interview Questions & Answers (2025)
Introduction
As data engineering and analytics converge in 2025, Databricks has become the go-to platform for building scalable, AI-ready data pipelines.
From Fortune 500 companies to startups, Databricks is powering data transformation, machine learning, and real-time analytics at scale.
If you’re preparing for Data Engineer, BI Developer, or Azure Databricks roles, these top 10 Databricks interview questions will help you confidently answer both technical and conceptual questions.
For a complete overview of the platform, start with our Beginner’s Guide to Databricks (2025).
1️⃣ What is Databricks and why is it used?
Answer:
Databricks is a unified data and AI platform built on Apache Spark. It simplifies data engineering, machine learning, and analytics by providing a collaborative environment for data scientists, analysts, and engineers.
It’s used to:
Process and transform massive data volumes
Build machine learning models
Enable BI dashboards using tools like Power BI
👉 Learn how Databricks works end-to-end in our Databricks + Power BI Integration Guide (2025).
2️⃣ What is the Databricks Lakehouse Architecture?
Answer:
The Lakehouse architecture merges the reliability of data warehouses with the flexibility of data lakes.
It uses Delta Lake as the foundation — an open-source layer that brings ACID transactions, schema enforcement, and time travel to data lakes.
✅ Read more: Why Databricks is Skyrocketing in 2025
3️⃣ What is Delta Lake and why is it important?
Answer:
Delta Lake is the core storage layer of Databricks.
It ensures data reliability through:
ACID transactions
Schema enforcement
Data versioning and time travel
It converts raw “data lakes” into reliable, query-ready “data lakehouses.”
🧩 Explore the difference between Databricks and Snowflake in our detailed comparison: Databricks vs Snowflake 2025.
4️⃣ What are Databricks Workspaces?
Answer:
A workspace is the collaborative environment in Databricks where users create notebooks, dashboards, jobs, and data pipelines.
It allows teams to work together using Python, SQL, R, or Scala — all in one environment.
5️⃣ What is Unity Catalog?
Answer:
Unity Catalog is Databricks’ governance layer that centralizes data access control, discovery, and lineage across all workspaces and clouds.
Key features:
Centralized access policies
Lineage tracking
Integration with identity providers (like Azure AD)
It ensures security, compliance, and visibility for enterprise-scale deployments.
6️⃣ What’s the difference between Databricks SQL and Apache Spark SQL?
Answer:
Feature Databricks SQL Apache Spark SQL Purpose Interactive BI & dashboards Distributed big data processing Interface Web UI, JDBC/ODBC (Power BI, Tableau) Programmatic (Python, Scala, Java) Optimization Query caching, cost-based optimizer Execution-level optimizations
Databricks SQL is ideal for business intelligence workloads and Power BI Integration.
7️⃣ How do you connect Databricks with Power BI?
Answer:
Steps to connect:
In Databricks, copy the Server Hostname and HTTP Path from your SQL Warehouse.
In Power BI → Get Data → Databricks Connector.
Enter details, choose DirectQuery or Import Mode, and authenticate with a personal access token.
📘 Detailed tutorial: Databricks + Power BI Integration: The Complete Guide (2025)
8️⃣ What are Delta Live Tables?
Answer:
Delta Live Tables (DLT) is Databricks’ framework for managing reliable ETL pipelines.
It automatically handles dependency tracking, testing, and error recovery — making ETL simple and production-ready.
Benefits:
Declarative pipeline definitions
Auto data validation
Version control & monitoring
9️⃣ How does Databricks support Machine Learning?
Answer:
Databricks integrates tightly with MLflow, an open-source framework for managing the machine learning lifecycle.
Features:
Track experiments and parameters
Register and version ML models
Deploy models in batch or real-time
💡 This makes Databricks a full-stack AI platform — from data prep to ML and visualization.
🔟 How can you optimize Databricks for performance?
Answer:
Performance tuning involves:
Using Z-Ordering for query pruning
Optimizing clusters with Auto-scaling
Using Photon Execution Engine for SQL performance
Creating Materialized Views for BI queries
For Power BI users, combine this with DAX optimization — covered in our Ultimate Guide to Power BI 2025.
Bonus Question: What skills should a Databricks professional learn in 2025?
SQL + Advanced Joins (Read: Top SQL Interview Questions 2025)
Power BI + DAX Fundamentals
Data Modeling and Azure Data Factory Basics
Spark / PySpark Programming
Machine Learning with MLflow
📍 Learn all these with our Power BI + Advanced SQL Training in Pune and Databricks modules.
Final Thoughts
Whether you’re preparing for a Data Engineer, BI Developer, or Databricks Specialist interview — these questions will help you think conceptually and answer confidently.
Remember, recruiters now value practical understanding over memorized answers.
Hands-on projects, mock interviews, and trainer guidance make the difference.
💼 Ready to prepare for real-world Databricks interviews?
Join Best Power BI & Databricks Training in Pune (2025) with live projects, mock interviews, and certification guidance.
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