Databricks Interview Questions and Answers

Databricks Interview Questions and Answers

Databricks Interview Questions

🧠 1. Basics of Databricks

  • What is Databricks?
  • Why is Databricks used?
  • What is a Databricks workspace?
  • What is Apache Spark and how is it related to Databricks?
  • Difference between Databricks and traditional Hadoop?

⚙️ 2. Core Concepts

  • What is a Databricks cluster?
  • What are different types of clusters in Databricks?
  • What is a notebook in Databricks?
  • What languages are supported in Databricks?
  • What is DBFS (Databricks File System)?

🔥 3. Apache Spark in Databricks

  • What is Spark architecture?
  • What are RDD, DataFrame, and Dataset?
  • Difference between RDD and DataFrame?
  • What is lazy evaluation in Spark?
  • What is Spark SQL?

📊 4. Data Processing

  • How do you load data into Databricks?
  • What is ETL in Databricks?
  • What are transformations and actions in Spark?
  • What is Delta Lake?
  • Why is Delta Lake important?

🗄️ 5. Delta Lake

  • What is Delta Lake?
  • Features of Delta Lake
  • What is ACID transaction support?
  • What is schema enforcement?
  • What is time travel in Delta Lake?

🔐 6. Security & Access

  • What is Unity Catalog?
  • How is data security handled in Databricks?
  • What are access control lists (ACLs)?
  • How do you manage permissions in Databricks?

⚡ 7. Performance Optimization

  • How do you optimize Spark jobs in Databricks?
  • What is partitioning?
  • What is caching in Spark?
  • What is broadcast join?
  • What is data skew?

🧪 8. Advanced Topics

  • What are Databricks Jobs?
  • What is workflow orchestration?
  • How do you schedule jobs in Databricks?
  • What is MLflow?
  • How is machine learning used in Databricks?

💻 9. Coding / Practical Questions

  • Load CSV file and create DataFrame
  • Filter data using Spark SQL
  • GroupBy and aggregation example
  • Write Delta table
  • Perform join between two datasets

🎯 10. Scenario-Based Questions

  • How would you handle large-scale data processing?
  • What if a Spark job fails?
  • How do you debug performance issues?
  • How do you handle schema changes in production?
  • How do you optimize cost in Databricks?

Databricks Interview Answers

🧠 1. Basics

Q: What is Databricks?
Databricks is a cloud-based data platform built on Apache Spark used for big data processing, analytics, and machine learning.

Q: Why is Databricks used?
It is used for fast data processing, ETL pipelines, analytics, and ML workflows in a scalable cloud environment.

Q: Difference between Databricks and Hadoop?

  • Hadoop → Disk-based processing, slower
  • Databricks → In-memory Spark processing, faster and cloud-native

⚙️ 2. Core Concepts

Q: What is a Databricks cluster?
A cluster is a group of computing resources (nodes) used to run Spark jobs.

Q: What is DBFS?
Databricks File System (DBFS) is a distributed file system used to store data in Databricks.

Q: What is a notebook?
A notebook is an interactive workspace where you can write code, run queries, and visualize data.

🔥 3. Spark Concepts

Q: What is Spark?
Apache Spark is a distributed computing engine used for big data processing.

Q: RDD vs DataFrame?

  • RDD → Low-level, less optimized
  • DataFrame → High-level, optimized, structured data

Q: What is lazy evaluation?
Spark does not execute transformations immediately; it executes only when an action is triggered.

📊 4. Data Processing

Q: What is ETL?
ETL means Extract, Transform, Load data for analytics.

Q: What is Delta Lake?
Delta Lake is a storage layer that adds ACID transactions, versioning, and reliability to data lakes.

🧾 5. Delta Lake

Q: What are ACID properties?

  • Atomicity
  • Consistency
  • Isolation
  • Durability

Q: What is time travel in Delta Lake?
It allows you to query older versions of data.

⚡ 6. Performance

Q: What is partitioning?
Dividing data into smaller chunks to improve performance.

Q: What is caching?
Storing frequently used data in memory for faster access.

Q: What is broadcast join?
A join optimization where a small dataset is sent to all nodes.

🧪 7. Advanced

Q: What is MLflow?
MLflow is used to track, manage, and deploy machine learning models.

Q: What are Databricks Jobs?
Jobs are used to schedule and automate notebooks or pipelines.

🎯 8. Scenario-Based

Q: How do you handle large data?

  • Use partitioning
  • Use optimized file formats (Parquet/Delta)
  • Use caching and broadcast joins

Q: How do you debug Spark jobs?

  • Check Spark UI
  • Analyze logs
  • Review stages and task failures