Top 20 Databricks Interview Questions AI Notes

Preparing for a Databricks interview? This article compiles 20 key questions covering various aspects of the platform, designed to help you showcase your knowledge and skills.

1. What is Databricks?

Answer: Databricks is a unified analytics platform built on top of Apache Spark. It provides a collaborative environment for data engineering, data science, and machine learning workflows. Key features include managed Spark clusters, a collaborative notebook environment, Delta Lake for reliable data lakes, MLflow for the machine learning lifecycle, and integrations with cloud storage and other services.

2. What are the key components of the Databricks platform?

Answer: Key components include:

Managed Spark Clusters: Easy provisioning and auto-scaling of Apache Spark clusters.
Databricks Notebooks: Collaborative environment for writing and executing code in Python, Scala, SQL, and R.
Delta Lake: An open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, enabling reliable data lakes.
MLflow: An open-source platform to manage the complete machine learning lifecycle, including experiment tracking, model management, and deployment.
Databricks SQL: A serverless data warehouse on the lakehouse, enabling BI and SQL analytics directly on Delta Lake.
Workflows: A service for orchestrating data engineering pipelines and machine learning workflows.
Databricks Connect: Allows you to connect your favorite IDEs, notebook servers, and other custom applications to Databricks clusters.

3. Explain the concept of the Lakehouse architecture and Databricks’ role in it.

Answer: The Lakehouse architecture aims to combine the best aspects of data lakes (scalability, cost-effectiveness, diverse data) and data warehouses (ACID transactions, data governance, BI capabilities). Databricks is a key player in enabling the Lakehouse by providing Delta Lake as the foundation for reliable data lakes and Databricks SQL for performing warehouse-like analytics directly on this data.

4. What is Delta Lake and what are its benefits?

Answer: Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. Its benefits include:

ACID Transactions: Ensures data integrity and consistency.
Schema Evolution: Allows you to make changes to your data schema over time reliably.
Time Travel: Enables querying older versions of your data for auditing or reproducibility.
Unified Batch and Streaming: Supports both batch and streaming data processing on the same data source.
Scalable Metadata Handling: Efficiently manages large datasets.
Data Skipping: Improves query performance by skipping irrelevant data based on metadata.

5. How do you create and manage clusters in Databricks?

Answer: Clusters can be created and managed through the Databricks UI, the Databricks CLI, or the Databricks REST API. You can configure cluster settings such as the Spark version, worker node type and count, autoscaling options, and Spark configurations. Databricks manages the underlying infrastructure, making cluster management easier.

6. What are Databricks Notebooks and their advantages?

Answer: Databricks Notebooks are a collaborative web-based interface for writing and executing code (Python, Scala, SQL, R), visualizing data, and creating documentation. Advantages include:

Collaboration: Multiple users can work on the same notebook simultaneously.
Interactive Environment: Allows for iterative development and immediate feedback.
Language Support: Supports multiple programming languages within the same notebook.
Visualization: Integrated tools for creating charts and graphs.
Version Control: Integration with Git for versioning and collaboration.

7. Explain how you would read data from and write data to different data sources in Databricks (e.g., cloud storage, databases, Kafka).

Answer: Databricks provides Spark connectors to interact with various data sources. Examples:

Cloud Storage (S3, ADLS, GCS): Use spark.read.format("delta").load("s3a://...") for Delta Lake or spark.read.csv("s3a://...") for CSV. Writing is similar with df.write.format("delta").save("s3a://...").
Databases (JDBC): Use spark.read.format("jdbc").option("url", "...").option("dbtable", "...").option("user", "...").option("password", "...").load(). Writing uses df.write.format("jdbc").option("url", "...").option("dbtable", "...").option("user", "...").option("password", "...").mode("append").save().
Kafka: Use the Spark Kafka connector: spark.readStream.format("kafka").option("kafka.bootstrap.servers", "...").option("subscribe", "...").load() for reading streams, and df.writeStream.format("kafka").option("kafka.bootstrap.servers", "...").option("topic", "...").start() for writing streams.

8. What is MLflow and how is it used in Databricks?

Answer: MLflow is an open-source platform for managing the machine learning lifecycle. In Databricks, it is deeply integrated, providing features for:

Experiment Tracking: Logging parameters, metrics, artifacts, and source code of ML runs.
Model Registry: Managing and versioning trained models.
Model Serving: Deploying models as REST endpoints or for batch inference.
Projects: Packaging ML code for reproducibility.

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

Answer: Monitoring and troubleshooting can be done through:

Spark UI: Accessing the Spark UI from the Databricks cluster details page to inspect job execution, stages, tasks, and resource utilization.
Databricks Logs: Viewing driver and executor logs for error messages and debugging information.
Databricks Monitoring: Using Databricks monitoring tools to track cluster performance and resource usage.
Alerts: Setting up alerts based on cluster metrics.

10. Explain the different workload types in Databricks (Data Engineering, Machine Learning, Databricks SQL).

Answer:

Data Engineering: Focuses on building and maintaining data pipelines for ETL/ELT processes, often using Spark and Delta Lake for data transformation and ingestion.
Machine Learning: Involves training, tracking, and deploying machine learning models using frameworks like TensorFlow, PyTorch, and scikit-learn, managed by MLflow on Spark.
Databricks SQL: Enables SQL-based analytics and BI directly on the data lakehouse, providing a serverless SQL query engine optimized for Delta Lake.

11. How do you optimize Spark performance in Databricks?

Answer: Spark performance optimization techniques in Databricks include:

Data Partitioning: Choosing appropriate partitioning strategies based on query patterns.
Data Skipping: Leveraging Delta Lake’s data skipping features.
Caching: Using spark.cache() or spark.persist() for frequently accessed data.
Broadcast Joins: Optimizing small table joins.
Avoiding Shuffles: Minimizing wide transformations that cause data shuffling.
Optimizing Spark Configurations: Tuning parameters like executor memory, cores, and parallelism.
Using efficient data formats: Preferring formats like Parquet or Delta.

12. What are Databricks Workflows and how are they used?

Answer: Databricks Workflows is a service for orchestrating data engineering and machine learning pipelines. It allows you to define, schedule, and monitor complex multi-step workflows (Jobs) consisting of various tasks like running notebooks, Spark JARs, Python scripts, and SQL queries. It provides features for dependency management, error handling, and monitoring.

13. Explain the concept of Databricks Delta Live Tables (DLT).

Answer: Delta Live Tables (DLT) is a framework for building reliable, maintainable, and testable data pipelines on Delta Lake. It uses a declarative approach where you define the desired end state of your data transformations, and DLT manages the underlying execution, including automatic data quality enforcement, error handling, and infrastructure scaling.

14. How does Databricks handle security and access control?

Answer: Databricks provides robust security features, including:

Workspace Isolation: Each workspace is isolated.
Access Control Lists (ACLs): Fine-grained control over access to notebooks, clusters, jobs, and data.
Integration with Cloud Provider Security: Leverages cloud provider security features like IAM roles and network configurations.
Data Encryption: Supports encryption at rest and in transit.
Audit Logging: Comprehensive logging of user actions.

15. What is Databricks SQL and its use cases?

Answer: Databricks SQL is a serverless data warehouse on the lakehouse, optimized for running SQL queries and BI workloads directly on Delta Lake. Use cases include:

Interactive SQL querying and analysis.
Building dashboards and visualizations with BI tools.
Running scheduled SQL-based reports.
Providing a SQL interface to data in the lakehouse for data analysts.

16. How can you integrate Databricks with other tools and services?

Answer: Databricks offers various integration options:

Cloud Storage: Seamless integration with S3, ADLS, and GCS.
BI Tools: Connectors for Tableau, Power BI, Looker, and others.
ETL/ELT Tools: Integration with tools like Informatica, Talend.
Scheduling and Orchestration: Databricks Workflows, Apache Airflow, Azure Data Factory.
Version Control: Git integration for notebooks.
CI/CD: APIs and tools for integrating with CI/CD pipelines.

17. Explain the difference between a Databricks Job and an interactive notebook execution.