Databricks Interview Questions and Answers 2025

Databricks is a unified analytics platform built on Apache Spark, designed to simplify big data processing and machine learning. It is used for data engineering, data science, and collaborative analytics, providing an interactive workspace for teams.

Databricks provides a managed environment for Apache Spark, automating cluster management, job scheduling, and resource scaling. It offers an interactive workspace with notebooks, making it easier to develop, test, and deploy Spark applications.

Databricks notebooks are interactive documents that allow users to write and execute code in languages like Python, Scala, SQL, and R. They are used for data exploration, visualization, and collaboration among team members.

A Databricks cluster is a set of virtual machines that run Spark jobs. Clusters can be configured for different workloads, and Databricks manages their lifecycle, including provisioning, scaling, and termination.

The Databricks Workspace is a collaborative environment where users can organize notebooks, libraries, and datasets. Key features include version control, access management, and integration with external data sources.

Data can be imported into Databricks using various methods such as uploading files, mounting cloud storage (like AWS S3 or Azure Blob), using Databricks utilities (dbutils), or connecting to external databases via JDBC.

Databricks Jobs are scheduled or triggered tasks that run code in a production environment, often for ETL or batch processing. Interactive notebooks are used for ad-hoc analysis and development, allowing real-time code execution and collaboration.

Databricks integrates with cloud provider security features, supports role-based access control (RBAC), and allows fine-grained permissions on notebooks, clusters, and data. It also supports encryption and audit logging.

Delta tables are a storage layer that brings ACID transactions to Apache Spark and Databricks. They enable reliable data pipelines, support schema evolution, and provide features like time travel and efficient data updates.

Databricks provides built-in visualization tools within notebooks, allowing users to create charts and graphs from query results. It also integrates with BI tools like Tableau and Power BI for advanced visualization needs.

Databricks Runtime is an optimized Spark environment provided by Databricks, including performance enhancements, proprietary connectors, and additional libraries for machine learning and data processing, whereas open-source Spark is the base engine without these optimizations.

Optimizing Spark jobs in Databricks involves tuning cluster configurations, using efficient file formats like Parquet or Delta, caching dataframes, minimizing data shuffles, and leveraging built-in performance features such as Adaptive Query Execution.

Databricks Jobs allow you to automate and schedule notebooks, JARs, or Python scripts. To schedule a recurring ETL pipeline, you define a job with the desired notebook/script, set up a cluster, configure parameters, and use the scheduling UI or API to set the frequency.

Delta Lake provides data versioning through its transaction log. You can use the 'time travel' feature to query previous versions of data by specifying a timestamp or version number, enabling rollback and auditing capabilities.

Best practices include using cluster-scoped or notebook-scoped libraries, managing dependencies with init scripts or the Databricks Library UI, and pinning library versions to ensure reproducibility across environments.

Secure connections can be established using encrypted JDBC/ODBC connections, configuring private endpoints, using service principals or managed identities, and storing credentials securely with Databricks secrets.

CI/CD can be implemented by storing notebooks in a version control system (like Git), using Databricks Repos for integration, and automating deployment with tools such as Azure DevOps, GitHub Actions, or Databricks CLI.

Databricks provides job and cluster monitoring dashboards, Spark UI for job execution details, and logging features. You can use these tools to analyze job stages, identify bottlenecks, and debug errors.

Databricks SQL is a serverless data warehouse solution optimized for BI workloads, providing a SQL-native interface, query optimization, and integrations with BI tools. It differs from Spark SQL by offering better performance and ease of use for analytics.

Delta Lake supports schema evolution, allowing you to add new columns or change data types. You can enable schema evolution by setting the appropriate write options, and Delta will automatically update the table schema.

Data governance can be achieved by using Unity Catalog for centralized access control, enabling audit logging, tracking data lineage, and enforcing data classification and retention policies.

You can share data using Delta Sharing, which allows secure, real-time data sharing across Databricks workspaces or with external partners without data replication.

Key considerations include choosing the right cluster type (standard, high concurrency, or single node), configuring autoscaling, selecting appropriate instance types, and monitoring resource utilization to avoid over-provisioning.

Databricks REST APIs allow you to automate tasks such as job submission, cluster management, and workspace operations. You can integrate these APIs with external systems or CI/CD pipelines for end-to-end automation.

MLflow is used for managing the machine learning lifecycle. In a scenario where you train multiple models, you can use MLflow Tracking to log experiments, MLflow Projects to package code, MLflow Models to manage model versions, and MLflow Registry for deployment.

Unity Catalog is a unified governance solution for all data assets in Databricks, providing centralized access control, fine-grained permissions, data lineage, and audit capabilities. It simplifies managing permissions across workspaces and supports compliance requirements.

Row-level and column-level security can be enforced using Unity Catalog by defining data access policies and applying them to tables or views. You can use SQL GRANT statements and dynamic views to restrict data visibility based on user roles.

The Lakehouse architecture combines the best features of data lakes and data warehouses, enabling ACID transactions, schema enforcement, and BI performance on open data formats. Databricks Lakehouse simplifies data pipelines and supports both analytics and AI workloads.

Optimizing Delta tables involves using ZORDER for data skipping, running OPTIMIZE and VACUUM commands to compact files and remove old data, partitioning tables appropriately, and leveraging data caching for frequently accessed datasets.

Databricks supports structured streaming with Apache Spark, allowing you to process real-time data from sources like Kafka or Event Hubs. You can build end-to-end streaming pipelines, apply windowed aggregations, and write results to Delta tables for low-latency analytics.

Best practices include using autoscaling clusters, setting cluster termination policies, monitoring resource usage with cost dashboards, leveraging spot instances, and optimizing job execution to minimize idle compute time.

Data masking can be achieved using SQL functions or UDFs to obfuscate sensitive fields. For anonymization, you can use hashing, tokenization, or data perturbation techniques, and enforce these transformations in ETL pipelines or views.

You can orchestrate workflows using Databricks Workflows, which allows chaining jobs with dependencies, conditional logic, and parameter passing. Integration with external orchestrators like Apache Airflow or Azure Data Factory is also supported via APIs.

Databricks is available on AWS, Azure, and GCP, allowing organizations to deploy in their preferred cloud. Unity Catalog and secure networking features help enforce data residency and compliance with regional regulations.

Migration involves assessing existing Spark jobs, refactoring code for compatibility, leveraging Databricks utilities for data ingestion, configuring clusters, and validating performance. Databricks provides migration tools and best practices for a smooth transition.

High availability is achieved by using managed clusters with autoscaling and fault tolerance. For disaster recovery, you can replicate data across regions, automate backups of Delta tables, and use infrastructure-as-code for environment restoration.

Databricks offers integration with monitoring tools like Azure Monitor, AWS CloudWatch, and custom logging via REST APIs. You can set up alerts for job failures, resource thresholds, and use audit logs for security monitoring.

Secrets are managed using Databricks Secrets, which store sensitive information in encrypted scopes. Access is controlled via RBAC, and secrets can be referenced in notebooks and jobs without exposing them in code.

Databricks Connect allows you to develop Spark applications locally in your IDE and run them on Databricks clusters. This improves productivity by enabling local debugging, code completion, and seamless integration with version control.

You can use MLflow for experiment tracking, model management, and deployment. Databricks AutoML automates feature engineering, model selection, and hyperparameter tuning, accelerating the development of high-quality models for production.