Databricks Interview Questions and Answers for 5 years experience
-
What is Databricks and what are its key components?
- Answer: Databricks is a unified analytics platform built on Apache Spark. Its key components include: a collaborative workspace for data scientists, engineers, and business analysts; a managed Spark cluster; a data lakehouse architecture; and tools for data ingestion, transformation, and visualization.
-
Explain the architecture of a Databricks cluster.
- Answer: A Databricks cluster consists of driver nodes (one) and worker nodes (multiple). The driver node coordinates the execution of the Spark application, while worker nodes perform the actual computation. Clusters can be configured with various instance types and sizes depending on workload requirements. They utilize a master-slave architecture for distributed processing.
-
Describe the different cluster modes available in Databricks.
- Answer: Databricks offers several cluster modes: Standard, High Concurrency, and All-Purpose. Standard is best for batch processing, High Concurrency for interactive workloads, and All-Purpose provides a balance between the two. Additionally, there are options for autoscaling and autotermination.
-
How do you optimize Spark performance in Databricks?
- Answer: Spark performance optimization involves several strategies: choosing appropriate cluster configurations (instance types, memory, cores), optimizing data structures (e.g., using Parquet), tuning Spark configurations (e.g., `spark.sql.shuffle.partitions`), using broadcast variables for small data, caching frequently accessed data, and utilizing vectorized operations.
-
Explain the concept of data lakehouse architecture in Databricks.
- Answer: The Databricks data lakehouse combines the advantages of data lakes (schema-on-read, scalability) and data warehouses (schema enforcement, ACID transactions, query performance). It leverages open formats like Parquet and Delta Lake to store data, providing both scalability and reliability.
-
What is Delta Lake and why is it used 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 (like AWS S3, Azure Blob Storage, or Google Cloud Storage). It enhances the reliability and maintainability of data in Databricks' data lakehouse.
-
How do you handle data security in Databricks?
- Answer: Data security in Databricks is achieved through various mechanisms: access control (using groups, permissions, and policies), encryption (at rest and in transit), network security (VPCs, private endpoints), and data masking/de-identification.
-
Explain different ways to ingest data into Databricks.
- Answer: Data ingestion methods include using Databricks' built-in connectors for various data sources (databases, cloud storage, etc.), using Spark's data sources APIs, employing automated pipelines using tools like Apache Kafka or other streaming technologies, and utilizing Databricks Auto Loader for efficient ingestion of data from cloud storage.
-
Describe your experience with Databricks notebooks.
- Answer: [This requires a personal answer detailing experience with Databricks notebooks, including coding in Python or Scala, using magic commands, version control, sharing notebooks, etc.]
-
How do you monitor and troubleshoot performance issues in Databricks?
- Answer: Performance monitoring involves using Databricks' monitoring tools (like the Unity Catalog and the Databricks SQL Analytics) to track cluster resource utilization, query execution times, and identify bottlenecks. Troubleshooting might involve analyzing Spark UI metrics, adjusting cluster configurations, optimizing code, and using logging and debugging tools.
-
[Question 11] ...
- Answer: [Detailed Answer 11]
-
[Question 12] ...
- Answer: [Detailed Answer 12]
Thank you for reading our blog post on 'Databricks Interview Questions and Answers for 5 years experience'.We hope you found it informative and useful.Stay tuned for more insightful content!