Azure Databricks is one of the most in-demand data engineering and analytics platforms, combining Apache Spark with Microsoft Azure’s cloud power. Whether you’re preparing for a data engineer, data analyst, or big data developer role, mastering Azure Databricks interview questions is essential.
This blog covers the top 25 Azure Databricks interview questions with clear, accurate answers, suitable for beginners and professionals alike.
Azure Databricks is an Apache Spark–based analytics platform optimized for Microsoft Azure. It is designed for big data processing, machine learning, and real-time analytics. Databricks provides a collaborative workspace, auto-scaling clusters, and deep integration with Azure services like Data Lake, Synapse, and Power BI.
Azure Databricks consists of:
Apache Spark is an open-source distributed data processing engine known for in-memory computation and high performance. Azure Databricks is built on Spark and enhances it with:
A Databricks cluster is a set of virtual machines used to run Spark workloads. It includes:
Clusters can be interactive or job-based, and they can auto-scale based on workload.
| Feature | Interactive Cluster | Job Cluster |
|---|---|---|
| Purpose | Ad-hoc analysis | Automated jobs |
| Lifetime | Long-running | Created per job |
| Cost | Higher | Cost-efficient |
| Usage | Development | Production |
Azure Databricks supports:
Multiple languages can be used within the same notebook.
A Databricks Notebook is a web-based interface for writing and executing code. It supports data visualization, markdown documentation, and collaborative editing.
DBFS is a distributed file system that allows Databricks to access Azure Blob Storage and Azure Data Lake as if they were local file systems. It uses the dbfs:/ path.
Azure Databricks integrates with ADLS using:
This allows seamless big data processing on large datasets.
Delta Lake is a storage layer built on top of data lakes that provides:
Delta Lake improves Parquet by adding:
Spark SQL is a Spark module that allows querying structured data using SQL syntax. In Databricks, it enables:
Auto Scaling automatically adds or removes worker nodes based on workload demand. This helps:
Auto Termination shuts down idle clusters after a defined time, preventing unnecessary compute costs.
Azure Databricks uses:
A Databricks Job is a scheduled or triggered task that runs notebooks, JARs, or Python scripts automatically for production workloads.
MLflow is an open-source machine learning lifecycle tool used for:
Azure Databricks has built-in MLflow integration.
A Spark DataFrame is a distributed collection of data organized into named columns. It is similar to a table in a database and supports SQL-like operations.
| Feature | RDD | DataFrame |
|---|---|---|
| Level | Low-level | High-level |
| Performance | Slower | Optimized |
| Schema | No | Yes |
| Ease of use | Complex | Simple |
Caching stores frequently accessed data in memory, reducing computation time and improving query performance.
Databricks uses Spark Structured Streaming to process real-time data from sources like:
A mount point connects external storage (like ADLS) to DBFS, allowing users to access data using simple file paths.
Photon is a high-performance query engine that accelerates SQL and Delta Lake workloads using vectorized processing.
They are often used together.
Companies use Azure Databricks because it offers:
Azure Databricks is a must-have skill for modern data professionals. These top 25 interview questions and answers will help you confidently tackle interviews for data engineering, analytics, and cloud roles.