Top 50 Databricks Interview Questions and Answers for Data Engineers

 

 1. What is Databricks, and how is it different from Apache Spark?

   Answer: Databricks is a cloud-based data platform built on Apache Spark. It offers collaborative workspaces, managed Spark clusters, and other features like MLflow and Delta Lake that enhance data engineering, machine learning, and analytics workflows.

 

 2. Explain the architecture of Databricks.

   Answer: Databricks has a multi-layered architecture with a control plane and a data plane. The control plane manages metadata, job scheduling, and cluster configurations, while the data plane executes data processing tasks on cloud infrastructure (e.g., AWS, Azure).

 

 3. What is a Databricks notebook, and what are its main features?

   Answer: A Databricks notebook is an interactive workspace where users can write, run, and visualize code in languages like SQL, Python, Scala, and R. It supports collaboration, visualization, version control, and automated job scheduling.

 

 4. What is Delta Lake in Databricks?

   Answer: Delta Lake is an open-source storage layer in Databricks that provides ACID transactions, scalable metadata handling, and schema enforcement for reliable data lakes. It enables better handling of large datasets and incremental data processing.

 

 5. How does Databricks handle versioning and history in Delta Lake?

   Answer: Delta Lake automatically tracks and manages historical versions of data with a transaction log. This enables time travel, allowing users to query or restore data from previous versions.

 

 6. What is the purpose of Z-order clustering in Databricks?

   Answer: Z-order clustering optimizes query performance by co-locating related data in storage. It is especially useful for accelerating range queries by reducing data scan sizes in partitioned tables.

 

 7. How do you optimize Spark jobs in Databricks?

   Answer: Optimize Spark jobs by using techniques like caching intermediate data, partitioning data correctly, optimizing joins, reducing shuffle operations, and tuning configurations.

 

 8. What is the Databricks File System (DBFS)?

   Answer: DBFS is a distributed file system layer built on cloud storage (e.g., S3, Azure Blob Storage) that provides a file interface for reading and writing data in Databricks.

 

 9. Explain the difference between `Managed` and `Unmanaged` tables in Databricks.

   Answer: Managed tables are fully controlled by Databricks, including storage and metadata, while unmanaged tables only store metadata, with data stored externally (e.g., in cloud storage).

 

 10. How does Databricks handle job scheduling?

   Answer: Databricks provides a job scheduler to automate running notebooks or JAR/Wheel files, with support for scheduling frequency, dependencies, and job monitoring.

 

 11. What are the main components of the Databricks Lakehouse Platform?

   Answer: The main components include Databricks notebooks, Delta Lake, MLflow for machine learning, collaborative workspaces, and Databricks SQL for analytics.

 

 12. How does Databricks integrate with CI/CD pipelines?

   Answer: Databricks integrates with CI/CD by using REST APIs, Git repositories, and tools like Databricks CLI for automating deployments in Dev, UAT, and Prod environments.

 

 13. What is an init script in Databricks, and why is it used?

   Answer: Init scripts are shell scripts that run on each cluster node when it starts. They are used for installing libraries, configuring environment variables, and setting up dependencies.

 

 14. What is the Databricks Runtime?

   Answer: Databricks Runtime is a pre-configured Spark environment with additional optimizations and libraries for data processing, machine learning, and analytics.

 

 15. Explain the concept of lazy evaluation in Spark.

   Answer: Lazy evaluation in Spark means that transformations on RDDs/Datasets/DataFrames are not executed until an action (like `collect`, `count`) is called, optimizing execution plans and minimizing data shuffles.

 

 16. What is Auto-scaling in Databricks?

   Answer: Auto-scaling automatically adjusts the number of worker nodes in a Databricks cluster based on workload, optimizing costs and performance.

 

 17. Explain the role of MLflow in Databricks.

   Answer: MLflow is a machine learning lifecycle management tool integrated into Databricks. It helps with experiment tracking, model management, and deployment.

 

 18. How does Databricks handle streaming data?

   Answer: Databricks supports structured streaming, which allows for real-time processing of data streams using Delta Lake and Spark, with seamless scaling and fault tolerance.

 

 19. What are UDFs and UDAFs in Spark, and when would you use them?

   Answer: UDFs (User Defined Functions) are custom functions applied to each row, while UDAFs (User Defined Aggregate Functions) operate across multiple rows. They are used when Spark's built-in functions are insufficient.

 

 20. What is the difference between `cache` and `persist` in Spark?

   Answer: `cache` is a shorthand for `persist` with the default storage level (`MEMORY_ONLY`). `persist` allows specifying different storage levels for fault tolerance and performance.

 

 21. How does Databricks handle data partitioning?

   Answer: Databricks allows data partitioning by column values, enabling efficient reads by limiting the amount of data scanned during query execution.

 

 22. Explain Databricks Connect and its uses.

   Answer: Databricks Connect allows developers to run Spark jobs on Databricks clusters from local IDEs, integrating Databricks into development workflows.

 

 23. What are common optimization techniques for Delta Lake?

   Answer: Common techniques include Z-order clustering, compacting small files, optimizing partitioning, and using `OPTIMIZE` and `VACUUM` commands.

 

 24. What is Schema Evolution in Delta Lake?

   Answer: Schema evolution allows Delta Lake to handle changes in schema over time, such as adding new columns, without breaking existing data.

 

 25. How can you implement `time travel` in Delta Lake?

   Answer: Delta Lake supports time travel through the `VERSION AS OF` or `TIMESTAMP AS OF` syntax, allowing users to query previous versions of the data.

 

 26. How does Databricks support data governance?

   Answer: Databricks integrates with data governance tools and supports features like Delta Lake’s ACID compliance, data lineage with Unity Catalog, and access control.

 

 27. What is Databricks SQL?

   Answer: Databricks SQL is a SQL-based analytics tool within Databricks for querying, visualizing, and analyzing data in Delta Lake and other sources.

 

 28. Explain Databricks Cluster Manager.

   Answer: The Cluster Manager handles the provisioning, configuration, and management of clusters, including scheduling, scaling, and termination policies.

 

 29. How does Unity Catalog work in Databricks?

   Answer: Unity Catalog provides a centralized governance layer for managing data permissions, lineage, and metadata across the Databricks Lakehouse.

 

 30. Explain Broadcast Joins and when to use them.

   Answer: Broadcast joins send a small table to all worker nodes to join with a large table, reducing data shuffles and improving performance for skewed joins.

 

 31. What are shuffles in Spark, and why are they expensive?

   Answer: Shuffles involve redistributing data across nodes, often leading to high network I/O, memory usage, and execution time. Minimizing shuffles can improve performance.

 

 32. How do you troubleshoot performance issues in Databricks?

   Answer: Use Spark UI, log analysis, and metrics from Databricks Jobs to identify bottlenecks like data shuffles, serialization issues, or resource constraints.

 

 33. What are the different cluster modes in Databricks?

   Answer: Cluster modes include `Standard` for general-purpose jobs, `High Concurrency` for multi-user SQL workloads, and `Single Node` for lightweight jobs and development.

 

 34. Explain the concept of Adaptive Query Execution (AQE).

   Answer: AQE optimizes query execution at runtime by dynamically adjusting join strategies, skew handling, and partitioning, based on actual data size and distribution.

 

 35. How does Databricks handle data security?

   Answer: Databricks provides data security through features like encryption at rest, IAM roles, network security groups, and Unity Catalog’s fine-grained access control.

 

 36. What’s the role of the REST API in Databricks?

   Answer: Databricks REST API enables automation and integration by allowing external applications to manage resources like clusters, jobs, and data sources.

 

 37. How do you deploy ML models on Databricks?

   Answer: ML models can be deployed using MLflow’s model registry, serving models as REST endpoints, or integrating with Databricks Jobs and serving clusters.

 

 38. What are Delta Tables in Databricks?

   Answer: Delta Tables are Delta Lake-based tables providing ACID transactions, schema enforcement, and audit history, ideal for batch and streaming data.

 

 39. How can you improve Spark’s I/O performance?

   Answer: Improving I/O involves partitioning data properly, reducing small files, using columnar formats (like Parquet), and minimizing data shuffles.

 

 40. Explain the difference between `EXISTS` and `IN` in SQL queries.

   Answer: `EXISTS` checks for the presence of rows satisfying a condition, while `IN` checks if a value exists within a set of values. `EXISTS` can be more efficient with large data.

 

 41. How does Databricks handle cost management?

   Answer: Cost management is done by optimizing cluster configurations, using auto-scaling, monitoring usage with cost management tools, and rightsizing cluster resources.

 

 42. What is `VACUUM` in Delta Lake?

   Answer: `VACUUM` removes old versions of data and deleted files, helping manage storage costs and performance by cleaning up Delta Lake’s transaction log.

 

 43. Explain `COPY INTO` in Databricks SQL.

   Answer: `COPY INTO` is a command for incremental data loading into Delta Tables from external sources like cloud storage.

 

 44. How do you use DBFS utilities?

   Answer: DBFS utilities like `dbutils.fs` offer file management commands to list, move, delete, or read files in Databricks File System.

 

 45. What is a `metastore` in Databricks?

   Answer: A metastore stores metadata about tables, views, and permissions, allowing Databricks users to access and manage datasets across environments.

 

 46. How does Databricks handle Big Data processing?

   Answer: Databricks handles Big Data through Spark’s parallel processing, Delta Lake’s storage capabilities, and optimized cluster configurations.

 

 47. What’s `display` vs. `displayHTML` in Databricks?

   Answer: `display` renders structured data tables and visualizations, while `displayHTML` allows rendering custom HTML and JavaScript content in notebooks.

 

 48. Explain `Magic Commands` in Databricks.

   Answer: Magic commands like `%run`, `%sql`, and `%pip` provide quick ways to execute code in notebooks, run SQL queries, and manage libraries.

 

 49. What is Databricks Workspace?

   Answer: Workspace is the collaborative environment where users can create notebooks, jobs, and clusters, and organize data, projects, and workspaces.

 

 50. What is the purpose of the Spark UI in Databricks?

   Answer: Spark UI helps monitor and troubleshoot Spark jobs by showing stages, tasks, execution plans, and resource usage, aiding in performance optimization.