Databricks Interview Questions and Answers for 7 years experience

1. What are the key differences between Databricks and traditional Hadoop?

   • Answer: Databricks offers a unified analytics platform built on Apache Spark, simplifying data engineering, data science, and machine learning workflows. Traditional Hadoop is more complex, requiring separate tools for each stage (e.g., Hive for querying, Pig for ETL, Mahout for ML). Databricks provides a managed service, reducing infrastructure management overhead, while Hadoop often requires significant on-premise infrastructure management. Databricks excels in speed and scalability compared to traditional Hadoop clusters. Finally, Databricks has a collaborative workspace fostering better teamwork, unlike the disparate toolset of Hadoop.

2. Explain your experience with Delta Lake. What are its advantages?

   • Answer: I have extensive experience using Delta Lake for building reliable data lakes on Databricks. Its ACID properties ensure data consistency and reliability, crucial for complex data pipelines. Delta Lake's schema enforcement and data quality features help maintain data integrity. Time travel capabilities allow for easy data versioning and rollback, crucial for debugging and recovery. Its optimization features like Z-ordering and compaction improve query performance significantly. Finally, Delta Lake's open-source nature and broad community support ensure its continued evolution and integration with other tools.

3. How have you optimized Spark jobs for performance in Databricks?

   • Answer: I've optimized Spark jobs using various techniques including data partitioning, choosing appropriate data formats (Parquet, ORC), using broadcast variables for small datasets, carefully selecting the number of executors and cores, tuning Spark configuration parameters (e.g., `spark.sql.shuffle.partitions`), leveraging Spark's caching mechanisms, and using vectorized operations. Profiling using Databricks' built-in tools and analyzing execution plans are also crucial for identifying bottlenecks. I also explore using Spark's optimization features like Tungsten and Catalyst.

4. Describe your experience with Databricks SQL.

   • Answer: I have extensively used Databricks SQL for interactive querying, data exploration, and report generation. I'm proficient in writing complex SQL queries, including window functions, common table expressions (CTEs), and joins. I've utilized Databricks SQL's integration with various data sources, including Delta Lake, Parquet, and CSV files. I understand how to leverage Databricks SQL's features for data governance, access control, and performance monitoring. My experience includes creating and managing dashboards and visualizations using Databricks SQL.

5. How do you handle data security and governance in Databricks?

   • Answer: Data security and governance are paramount. I leverage Databricks' built-in features like access control lists (ACLs), data masking, and encryption at rest and in transit. I implement row-level security (RLS) to restrict access to sensitive data based on user roles. I use Databricks' unified catalog for metadata management and data lineage tracking. Regular audits and security assessments are vital. I also adhere to organizational security policies and best practices, ensuring compliance with relevant regulations.

6. Explain your experience with AutoML in Databricks.

   • Answer: I've utilized Databricks AutoML to rapidly prototype and deploy machine learning models without extensive manual coding. It simplifies the model selection, hyperparameter tuning, and model evaluation processes, significantly reducing development time. I've used AutoML for various tasks including classification, regression, and clustering, leveraging its ability to handle various data types and model algorithms. I understand the trade-offs involved and know when AutoML is best suited and when manual model building is necessary.

7. How do you monitor and troubleshoot Spark jobs in Databricks?

   • Answer: I utilize Databricks' built-in monitoring tools to track job performance, identify bottlenecks, and troubleshoot issues. This includes using the Spark UI, monitoring metrics like execution time, data skew, and resource utilization. I analyze logs to pinpoint errors and exceptions. I use the Databricks workspace to manage and monitor clusters and jobs. For complex issues, I leverage Databricks support resources and community forums.

8. Describe your experience with Databricks notebooks.

   • Answer: I’m highly proficient in using Databricks notebooks for collaborative data exploration, code development, and documentation. I leverage their integration with various languages like Python, Scala, and SQL, and understand how to effectively use magic commands for enhanced functionality. I'm comfortable sharing and version controlling notebooks using Git integration. I structure notebooks for readability and maintainability, using markdown for clear explanations and code commenting for clarity.

9. How do you manage and scale Databricks clusters?

   • Answer: I have experience managing and scaling Databricks clusters based on workload requirements. I understand the trade-offs between different cluster types (e.g., all-purpose, compute-optimized) and know how to select the appropriate cluster configuration. I can dynamically scale clusters up or down based on demand using autoscaling features. I monitor cluster resource utilization to identify potential bottlenecks and optimize cluster configuration for cost efficiency and performance. I'm familiar with managing cluster security settings and configurations.

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