Databricks Interview Questions and Answers

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1. What are the different Databricks runtimes, and how do you select one for your workload?

Databricks provides multiple runtimes tailored for different workloads. The choice depends on the specific requirements of your task.

• Standard Runtime: The default runtime suitable for general-purpose data engineering and data science tasks. Ideal for ETL pipelines, exploratory data analysis, and non-specialized workloads.
• ML Runtime: Comes pre-installed with popular machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn. It also includes distributed ML libraries like Horovod. Use this runtime for training and deploying ML models.
• Photon-Optimized Runtime: Designed for SQL analytics and performance-intensive tasks. It leverages Photon, a next-generation query engine, to improve the speed of queries. Ideal for running large-scale SQL queries on Delta Lake.
• GPU-Accelerated Runtime: Includes support for GPU hardware and is optimized for deep learning tasks and workloads requiring GPU processing. Use this for training neural networks or other GPU-intensive applications.

Selection Criteria:

• Use Standard Runtime for general-purpose ETL and basic data processing.
• Choose ML Runtime for advanced machine learning workflows.
• Opt for Photon-Optimized Runtime for performance-focused analytics.
• Leverage GPU-Accelerated Runtime for deep learning and heavy computational tasks.

2. How do you secure sensitive information in Databricks?

Databricks provides several mechanisms to secure sensitive information:

Secret Scopes:

• Store sensitive data like credentials, API keys, and tokens securely.
• Secrets can be accessed programmatically using Databricks utilities (e.g., dbutils.secrets.get).
• Two types of secret scopes:
• Databricks-backed.
• Azure Key Vault-backed (on Azure Databricks).

Role-Based Access Control (RBAC):

• Define roles and permissions to restrict access to resources such as notebooks, clusters, and data.
• Use workspace permissions to control who can view or modify resources.

Encrypted Storage:

• Databricks encrypts data at rest and in transit using industry-standard encryption methods.
• Leverage cloud-native encryption tools (e.g., AWS KMS, Azure Key Vault).

Network Security:

• Use Virtual Private Clouds (VPCs) and Private Link to secure communication.
• Implement IP whitelisting and secure cluster connectivity.

3. Explain how Databricks handles job scheduling and monitoring.

Databricks Jobs:

• Automate workflows by scheduling notebooks, Python scripts, JARs, or custom tasks.
• Jobs can run on a one-time or recurring schedule.

Task Dependencies:

• Define task dependencies within a job, enabling multi-step workflows with sequential or parallel execution.
• Use DAG-based task orchestration to manage complex workflows.

Retries:

• Configure retries for failed tasks to ensure resilience.
• Specify retry limits and delay intervals between retries.

Notifications:

• Set up alerts for job success, failure, or completion.
• Notifications can be sent via email, webhook, or other integrations.

Monitoring:

• Use the Jobs UI to monitor job runs, visualize task statuses, and access logs for debugging.

4. What is the purpose of Databricks SQL? How is it different from traditional SQL?

Purpose of Databricks SQL:

• Optimized for querying and analyzing data stored in data lakes.
• Seamlessly integrates with Delta Lake for real-time analytics.
• Provides a BI-friendly environment with dashboards and visualization tools.

Differences from Traditional SQL:

• Databricks SQL is designed for distributed computing and scales effortlessly with large data volumes.
• Built-in support for Delta Lake ensures ACID compliance, versioning, and schema enforcement.
• Provides optimized performance with Photon and supports SQL syntax extensions for big data processing.

5. What are the best practices for using Databricks notebooks in collaborative environments?

Version Control:

• Use Git integration to track changes and collaborate effectively.
• Maintain a history of notebook edits.

Comments and Documentation:

• Use Markdown cells for explanations and comments for inline code clarity.
• Add detailed descriptions to make notebooks self-explanatory.

Sharing and Permissions:

• Share notebooks with team members and set appropriate permissions (read-only, edit).

Organizing Notebooks:

• Group notebooks into folders or projects to maintain structure.
• Use consistent naming conventions for notebooks and folders.

Testing and Review:

• Test notebooks on staging clusters before deploying them in production.
• Conduct peer reviews for critical changes.

6. How do Delta Lake and Delta Tables improve data management in Databricks?

Delta Lake and Delta Tables enhance data reliability and manageability:

ACID Transactions:

• Ensure data consistency during concurrent read/write operations.
• Prevent partial updates or corrupted data during failures.

Versioning:

• Maintain a history of changes to enable time travel (querying past data states).
• Facilitate debugging and rollback capabilities.

Schema Enforcement:

• Validate data against predefined schemas.
• Automatically reject non-conforming data to prevent corruption.

Efficient Storage:

• Optimize storage with data compaction and partitioning.
• Improve query performance through indexing and caching.

7. What are the different cluster types in Databricks, and when would you use them?

All-Purpose Clusters:

• Designed for interactive workloads and collaborative development.
• Ideal for exploratory data analysis and ad-hoc queries.

Job Clusters:

• Created for running jobs and automatically terminated after job completion.
• Cost-efficient for production jobs and scheduled tasks.

High-Concurrency Clusters:

• Support multiple concurrent users and optimized for SQL analytics.
• Use cases include serving dashboards, BI tools, and shared resources.

8. Explain how to debug a failing Databricks job or notebook.

Examine Logs:

• Access cluster logs and driver/executor logs via the UI.
• Look for stack traces or specific error messages.

Enable Debugging Mode:

• Use %debug magic command to inspect variables and analyze errors.

Cluster Metrics:

• Monitor CPU, memory, and disk utilization to identify resource bottlenecks.
• Check cluster event timelines for anomalies.

Error Notifications:

• Configure notifications for failed runs to proactively address issues.

Retry Mechanisms:

• Enable retries for transient failures and ensure fault tolerance.