Databricks Interview Questions and Answers for 10 years experience

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

   • Answer: Databricks offers a unified analytics platform built on Apache Spark, simplifying development and deployment compared to the complex, often disparate tools in traditional Hadoop. Key differences include: simplified cluster management, integrated workflows (data ingestion, processing, visualization), enhanced security, scalability, and ease of use via collaborative notebooks and APIs. Hadoop requires more manual configuration and integration of multiple components.

2. Explain the architecture of a Databricks cluster.

   • Answer: A Databricks cluster consists of driver nodes and worker nodes. The driver node runs the Spark driver program, which coordinates the execution of the application. Worker nodes execute tasks assigned by the driver. It leverages cloud infrastructure (AWS, Azure, GCP) for scalability and resource management. The cluster can be configured with various instance types and sizes to optimize cost and performance. It also integrates with cloud storage services for data persistence.

3. How do you optimize Spark performance in Databricks?

   • Answer: Optimizing Spark performance involves several strategies: choosing appropriate cluster configurations (instance types, number of nodes), data partitioning and serialization (choosing optimal data formats like Parquet, ORC), optimizing data structures (using dataframes effectively, broadcasting small datasets), code optimization (avoiding shuffles, using caching strategically), using Spark's built-in optimization features (e.g., adaptive query execution), and leveraging Databricks features like Auto Scaling and Optimized Clusters.

4. Describe different ways to ingest data into Databricks.

   • Answer: Data can be ingested into Databricks using various methods: using Spark's built-in connectors to read data from various sources (CSV, JSON, Parquet, databases, cloud storage like S3, ADLS Gen2, GCS), using structured streaming for real-time data ingestion, employing Databricks' Delta Live Tables (DLT) for automated data pipelines, using Auto Loader for efficient and scalable ingestion of data from cloud storage, and integrating with other ETL tools like Informatica or Talend.

5. Explain the concept of Delta Lake in Databricks.

   • Answer: Delta Lake is an open-source storage layer that provides ACID transactions, schema enforcement, and data versioning on top of cloud storage (e.g., S3, Azure Blob Storage, Google Cloud Storage). It enhances the reliability and performance of data lakes by providing a more robust and reliable foundation for data processing and analytics. Key features include time travel, data versioning, schema evolution, and data quality improvements.

6. How do you handle data security in Databricks?

   • Answer: Data security in Databricks involves multiple layers: access control using Databricks' built-in mechanisms (users, groups, permissions), network security (using VPCs, private endpoints), encryption at rest and in transit, data masking and anonymization techniques, integration with cloud identity providers (e.g., Azure AD, Okta), and regular security audits and vulnerability scans. Implementing least privilege access and strong password policies are also crucial.

7. What are Databricks workspaces and how are they used?

   • Answer: Databricks workspaces are collaborative environments where data scientists, engineers, and analysts can work together on data projects. They provide a centralized location for managing code, data, and notebooks. Workspaces offer features like collaborative notebook editing, version control, cluster management, and integration with various data sources and tools. They are essential for organizing and managing large-scale data projects and teams.

8. Explain the concept of Databricks Unity Catalog.

   • Answer: Databricks Unity Catalog is a centralized governance service that provides unified metadata management, access control, and data discovery across all your Databricks workspaces. It simplifies data governance, improves data security, and enables centralized management of data assets regardless of where they are stored. Key features include fine-grained access control, data discovery, lineage tracking, and data catalog management.

9. How do you monitor and troubleshoot performance issues in a Databricks cluster?

   • Answer: Monitoring and troubleshooting involve using Databricks' monitoring tools, examining Spark UI metrics (job progress, stage durations, data shuffle, resource utilization), analyzing logs for errors and exceptions, employing Databricks' query optimization features (adaptive query execution, broadcast joins), and proactively optimizing your code and data structures. Understanding resource usage and identifying bottlenecks is key to resolving performance issues. Utilizing Databricks' support and documentation is helpful as well.

10. Describe your experience with Databricks' Auto Loader.

    • Answer: I have extensive experience using Databricks Auto Loader for efficiently and scalably ingesting data from various cloud storage sources. I've leveraged its capabilities to process large volumes of streaming data, handling schema evolution and ensuring data quality with minimal manual intervention. I understand its advantages over traditional batch processing methods and have implemented it to create robust and efficient data pipelines.

11. How have you used Databricks for machine learning tasks?

    • Answer: I've utilized Databricks extensively for ML tasks, leveraging its integration with MLlib, TensorFlow, PyTorch, and other ML libraries. I've built and deployed ML models using various techniques, including model training, hyperparameter tuning, feature engineering, and model deployment. I have experience with model monitoring and retraining to maintain model accuracy and performance.

12. Explain your experience with Databricks SQL.

    • Answer: I've used Databricks SQL extensively for data querying, analysis, and reporting. I’m proficient in writing SQL queries and optimizing them for performance. I have experience building dashboards and visualizations using Databricks SQL's capabilities and have worked with various data sources and formats.

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