Databricks Interview Questions and Answers

1. What is the purpose of Databricks notebooks?

In Databricks, Notebooks can be used to perform:

Interactive data exploration
Code execution
Collaboration

They support multiple programming languages and visualizations within the same environment.

2. What is Databricks’ approach to data security?

The various approaches used by Databricks to ensures data security are:

Role-based access control (RBAC).
Data encryption (in-transit and at-rest).
Network isolation with Virtual Private Clouds (VPC).
Integration with IAM tools like Azure AD and AWS IAM.

3. How do you schedule jobs in Databricks?

Jobs can be scheduled using the Jobs UI in which users can set triggers, define dependencies, and monitor executions. It also supports CRON expressions.

4. How would you handle where a Spark job is running slowly in Databricks?

Steps to handle situation in which Spark job is running slowly in Databricks:

To analyze Spark UI for bottlenecks.
Optimizing data partitions.
Use caching for frequently accessed data.
Ensure cluster configurations are optimal.

5. What are the main components of Databricks? What programming languages are supported in Databricks?

The main components of databricks are:

Workspace: To organize notebooks, libraries, and dashboards.
Clusters: Managed Spark clusters for computation.
Notebooks: Interactive development and data exploration.
Jobs: For scheduling and orchestrating workflows.
Data: Tools for exploring and managing data.

Databricks supports programming languages, like Python, Scala, R, SQL, and Java.

6. How does Databricks handle version control for notebooks?

To handle version control for notebooks, Databricks provides built-in version control in which user can view and restore previous versions of a notebook. Integration with Git-based version control systems is also supported.

7. What is a Databricks cluster, and how is it managed?

Databricks cluster is a set of compute resources and configurations which are used to run notebooks and jobs. It is managed through auto-scaling, which adjusts resources based on workload.

8. What are the importance of Databricks Delta Lake?

In Databricks, Delta Lake is an open-source storage layer that brings reliability to data lakes. It ensures data consistency in ACID transactions and enables various features like schema enforcement and time travel.

9. What is Auto Loader in Databricks?

Auto Loader is a important feature that automatically ingests and processes new files from cloud storage systems in real-time. It supports schema inference and incremental processing.

10. Explain the use of MLflow in Databricks.

MLflow is an open-source platform that is integrated with Databricks for managing the machine learning lifecycle, including:

Experimentation
Reproducibility
Deployment
Model registry

11. How do you implement real-time data processing in Databricks?

Real-time data processing can be implemented using Structured Streaming and Delta Lake. Auto Loader can be used to ingest streaming data, which is then processed in a Spark streaming job.

12. What are Unity Catalog and its key features?

Unity Catalog is a governance solution that provides

Centralized data management
Fine-grained access controls
Data lineage
Auditing capabilities

13. How do you monitor and troubleshoot Databricks jobs?

Monitoring tools available to handle. databricks jobs are:

Databricks Jobs UI
Spark UI
Cluster logs

Troubleshooting process involves analyzing error messages, checking driver and executor logs, and adjusting cluster configurations.

14. What are the external data sources databricks can be connected to?

Databricks can connect to different external data sources like

Azure Data Lake
AWS S3

Relational databases using JDBC, ODBC, or native connectors.

15. Describe a use case where you used Databricks to solve a business problem.

The various use cases in which databricks can be used to solve a business problem are:

Building a recommendation system by processing large datasets using Databricks, Training a collaborative filtering model with MLlib, and deploying it for real-time user recommendations.

16. How does Databricks support multi-cloud environments?

Databricks supports the multu-cloud environments like Azure, AWS, and Google Cloud. It integrates seamlessly with their native services such as, S3 for AWS, ADLS for Azure and supports interoperability across environments.

17. How do you use Databricks SQL for data exploration?

Databricks SQL provides a user interface to run SQL queries on data lakes. Users can query Delta Tables directly and build dashboards for visualizing results. Use SQL endpoints for scalable query execution.

18. How would you implement a slowly changing dimension (SCD) in Databricks?

Use Delta Lake’s MERGE INTO functionality to handle SCDs. Match records based on keys and update rows for existing records while inserting new ones.

Example:

MERGE INTO target_table AS target
USING source_table AS source
ON target.id = source.id
WHEN MATCHED THEN UPDATE SET …
WHEN NOT MATCHED THEN INSERT …

19. What is Databricks Runtime, and why is it important?

Databricks Runtime is a preconfigured Spark runtime environment that is optimized for Databricks which includes:

Proprietary optimizations, pre-installed libraries, Integration with Databricks features for performance and productivity.

20. What are the advantages of using Databricks for ETL processes?

Advantages of using Databricks for ETL processes

Scalability with Spark’s distributed architecture.
Delta Lake for data reliability and schema enforcement.
Simplified scheduling and orchestration of ETL workflows.
Integration with various data sources and formats.

21. What are the different cluster types in Databricks?

The different clusters in databricks are

All-Purpose Clusters: Suitable for interactive use cases like development and exploration.

Job Clusters: Temporary clusters are created for running jobs and terminated afterward.

High-Concurrency Clusters: For concurrent execution of multiple users or jobs with low latency.

22. What are the best practices for managing clusters in Databricks?

Best practices for managing clusters in Databricks Usage of job clusters for task-specific workloads to save costs. Enable autoscaling to handle varying workloads efficiently. Set termination policies to shut down idle clusters. Use cluster pools to minimize startup times for frequently used clusters.

23. How would you implement a streaming ETL pipeline in Databricks?

Structured Streaming can be used to read data from a source like Kafka or cloud storage. Process the data with transformations in real-time. Write the output to a Delta Table or another sink for analysis.

24.How would you troubleshoot a failed job in Databricks?

Review the Jobs UI for error messages and logs and use the Spark UI to analyze the DAG, stages, and tasks. Check cluster logs for detailed driver and executor errors and ensure data source availability and correct configurations. Test the workflow in smaller chunks to isolate the issue.

25. What is the significance of Delta Live Tables (DLT) in Databricks?

Delta Live Tables (DLT) simplify the creation of reliable and efficient data pipelines. It uses declarative syntax to define data transformations and ensures data quality through built-in validations. DLT automatically handles tasks like data lineage and dependency tracking.

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Databricks Interview Questions and Answers