Comprehensive Guide for Data Engineer, Data Architect, Cloud Engineer, Analytics Engineer & Senior Data Platform Roles.

This guide covers beginner, intermediate, advanced, architect-level, and real-world scenario-based Databricks interview questions commonly asked in U.S. companies.

1. What is Databricks?

Answer

Databricks is a unified analytics platform built on Apache Spark that combines:

• Data Engineering
• Data Science
• Data Analytics
• Machine Learning
• AI/GenAI

into a single platform.

It provides:

• Lakehouse Architecture
• Delta Lake
• Apache Spark
• MLflow
• Unity Catalog
• Structured Streaming
• Workflow Orchestration
• AI/LLM Integration

Key Benefits

• Collaborative workspace
• Scalability
• High performance
• Unified governance
• Reduced data duplication

2. What is Lakehouse Architecture?

Answer

Lakehouse combines:

Lakehouse gives:

• ACID Transactions
• Data Governance
• Data Quality
• BI Performance
• ML Support

Traditional Architecture

Data Sources
    ↓
Data Lake
    ↓
Data Warehouse
    ↓
BI

Lakehouse Architecture

Data Sources
      ↓
 Delta Lake
      ↓
BI + ML + Analytics

3. What are the main components of Databricks?

Answer

Workspace

Collaborative notebooks

Cluster

Compute resources

DBFS

Storage Layer

Delta Lake

Transactional Storage

Unity Catalog

Governance Layer

Jobs

Scheduling

MLflow

Machine Learning Tracking

SQL Warehouse

Analytics

4. What is Delta Lake?

Answer

Delta Lake is an open-source storage layer built on Parquet.

Features:

• ACID Transactions
• Schema Enforcement
• Time Travel
• Data Versioning
• Upserts
• Deletes
• Merge Operations

5. Why Delta Lake over Parquet?

6. What is ACID Transaction in Delta Lake?

Answer

ACID stands for:

• Atomicity
• Consistency
• Isolation
• Durability

Delta Lake ensures reliable concurrent reads and writes.

7. What is Delta Transaction Log?

Answer

Stored in:

_delta_log

Contains:

• Metadata
• Schema
• Commit History
• File Tracking

Example:

00000000000000000001.json

Every transaction creates a new log entry.

8. What is Time Travel?

Answer

Allows querying historical versions.

Example:

SELECT *
FROM sales VERSION AS OF 10;

or

SELECT *
FROM sales TIMESTAMP AS OF '2025-05-01';

Use Cases

• Audit
• Recovery
• Debugging

9. What is Schema Enforcement?

Answer

Prevents bad data from being written.

Example:

Expected:

id INT
name STRING

Incoming:

id STRING
name STRING

Write fails.

10. What is Schema Evolution?

Answer

Allows adding new columns.

df.write \
.mode("append") \
.option("mergeSchema","true") \
.save(path)

11. What is OPTIMIZE command?

Answer

Compacts small files.

OPTIMIZE sales;

Benefits:

• Faster queries
• Reduced metadata

12. What is Z-Ordering?

Answer

Improves data skipping.

OPTIMIZE sales
ZORDER BY (customer_id);

Benefits:

• Faster filtering
• Better pruning

13. What is VACUUM?

Answer

Removes old files.

VACUUM sales RETAIN 168 HOURS;

Default:

7 Days

Benefits:

• Storage optimization

14. What is Data Skipping?

Answer

Delta stores statistics:

• Min value
• Max value

Databricks skips unnecessary files during query execution.

15. What is Auto Optimize?

Answer

Automatically:

• Compacts files
• Optimizes writes

SET spark.databricks.delta.autoCompact.enabled=true

16. What are Databricks Clusters?

Answer

Compute environments used to run workloads.

Types:

• Interactive Cluster
• Job Cluster
• High Concurrency Cluster
• Serverless Compute

17. Difference between Interactive and Job Cluster?

18. What is Autoscaling?

Answer

Automatically adds/removes workers.

Benefits:

• Cost Savings
• Performance

19. What is Photon Engine?

Answer

Databricks’ next-generation query engine.

Written in:

C++

Benefits:

• Faster SQL
• Lower Cost
• Better Analytics Performance

Often 2–10x faster than traditional Spark workloads.

20. What is Serverless Databricks?

Answer

No cluster management.

Benefits:

• Instant startup
• Reduced operations
• Automatic scaling

21. Explain Spark Architecture in Databricks.

Answer

Components:

Driver
   ↓
Cluster Manager
   ↓
Executors

Driver:

• Creates jobs
• Schedules tasks

Executors:

• Process data

22. What are RDDs?

Answer

Resilient Distributed Datasets.

Features:

• Distributed
• Fault Tolerant
• Immutable

Older Spark abstraction.

Modern Databricks prefers:

• DataFrames
• Spark SQL

23. DataFrame vs RDD?

24. What is Catalyst Optimizer?

Answer

Spark SQL optimizer.

Functions:

• Query Optimization
• Predicate Pushdown
• Join Optimization

25. What is Tungsten Engine?

Answer

Memory and CPU optimization engine.

Benefits:

• Faster execution
• Better memory utilization

26. What is Lazy Evaluation?

Answer

Spark delays execution until an action occurs.

Example:

df.filter(...)

No execution.

Action:

df.count()

Triggers execution.

27. What are Transformations?

Answer

Create new DataFrames.

Examples:

filter()
select()
join()
groupBy()

28. What are Actions?

Answer

Trigger execution.

Examples:

count()
collect()
show()
write()

29. What is Partitioning?

Answer

Data distributed across executors.

Benefits:

• Parallelism
• Faster processing

30. What is Repartition?

Answer

Increases/decreases partitions.

df.repartition(100)

Full shuffle occurs.

31. What is Coalesce?

Answer

Reduces partitions efficiently.

df.coalesce(10)

Minimal shuffle.

32. What is Broadcast Join?

Answer

Small table copied to all executors.

broadcast(df_small)

Avoids shuffle.

33. When should Broadcast Join be used?

Answer

When one table is small.

Example:

Customers = 10 MB
Transactions = 1 TB

Broadcast customers table.

34. What is Shuffle?

Answer

Data movement between executors.

Expensive operation.

Occurs during:

• Join
• GroupBy
• Distinct
• Repartition

35. How do you optimize Databricks jobs?

Answer

Common strategies:

• Partitioning
• Broadcast Joins
• Photon
• OPTIMIZE
• ZORDER
• Cache
• Auto Scaling
• AQE

36. What is AQE (Adaptive Query Execution)?

Answer

Dynamically optimizes queries during runtime.

Features:

• Dynamic joins
• Skew handling
• Partition optimization

37. What is Data Skew?

Answer

Uneven data distribution.

Example:

90% data -> USA
10% data -> Rest

One executor becomes overloaded.

38. How do you solve Data Skew?

Answer

Methods:

• Salting
• Broadcast Join
• AQE
• Repartitioning

39. What is Caching?

Answer

Stores data in memory.

df.cache()

Improves repeated query performance.

40. What is Persist?

Answer

Stores data using configurable storage levels.

df.persist()

More flexible than cache.

Advanced Interview Questions (Senior Level)

Q41. Explain Medallion Architecture.

Answer

Three-layer architecture:

Bronze

Raw data

Silver

Cleaned data

Gold

Business-ready data

Raw → Bronze
       ↓
     Silver
       ↓
      Gold

Q42. Explain MERGE INTO in Delta Lake.

MERGE INTO target t
USING source s
ON t.id=s.id

WHEN MATCHED
THEN UPDATE SET *

WHEN NOT MATCHED
THEN INSERT *

Used for:

• CDC
• Upserts
• Slowly Changing Dimensions

Q43. Explain CDC in Databricks.

CDC = Change Data Capture

Tracks:

• Inserts
• Updates
• Deletes

Common Sources:

• SQL Server
• Oracle
• MySQL
• SAP

Q44. How do you implement SCD Type 2?

Answer

Create:

• Effective Date
• End Date
• Current Flag

Maintain historical records using Delta MERGE.

Q45. Explain Unity Catalog.

Answer

Central governance layer.

Provides:

• RBAC
• Auditing
• Lineage
• Data Discovery
• Fine-grained Permissions

Q46. Difference between Hive Metastore and Unity Catalog?

Q47. What is Databricks Workflow?

Answer

Job orchestration service.

Supports:

• Notebook Tasks
• Python Tasks
• SQL Tasks
• Dependencies

Q48. Explain Structured Streaming.

Answer

Near real-time processing engine.

Sources:

• Kafka
• EventHub
• Kinesis
• Delta

Supports:

• Exactly Once Processing

Q49. What is Auto Loader?

Answer

Incremental file ingestion framework.

spark.readStream
.format("cloudFiles")

Benefits:

• Scalable
• Incremental
• Efficient

Q50. Real Interview Scenario

Question

A Databricks job processing 5 TB data suddenly takes 5 hours instead of 30 minutes. How do you troubleshoot?

Answer

Step-by-Step:

1. Check Spark UI
2. Check Stage Failures
3. Check Skew
4. Check Shuffle Size
5. Check Cluster Scaling
6. Check Data Growth
7. Check Photon Status
8. Check AQE
9. Check Partition Count
10. Check Delta Statistics

Typical root causes:

• Data skew
• Small files
• Bad joins
• Insufficient partitions
• Cluster changes
• Source data explosion

Most Frequently Asked Databricks Interview Questions

1. What is Databricks?
2. What is Delta Lake?
3. What is Lakehouse?
4. What is Medallion Architecture?
5. What is Unity Catalog?
6. What is Photon?
7. What is Auto Loader?
8. What is AQE?
9. What is ZORDER?
10. What is OPTIMIZE?
11. What is VACUUM?
12. What is Time Travel?
13. What is MERGE?
14. What is CDC?
15. What is Structured Streaming?
16. What is Broadcast Join?
17. What is Data Skew?
18. How do you optimize Spark jobs?
19. Difference between Repartition and Coalesce?
20. How do you design a Databricks Lakehouse for an enterprise?

Senior/Architect-Level Topics to Master

• Lakehouse Architecture
• Delta Internals
• Transaction Logs
• Spark Optimization
• AQE
• Photon
• Unity Catalog
• Data Governance
• CDC Pipelines
• Streaming Architecture
• Multi-cloud Databricks (AWS/Azure/GCP)
• Cost Optimization
• MLflow
• GenAI on Databricks
• Data Mesh with Unity Catalog
• Disaster Recovery Design
• Enterprise Security Architecture

These topics account for a large portion of Databricks Data Engineer, Senior Data Engineer, Staff Engineer, Data Architect, Analytics Engineer, and Cloud Data Platform interviews in the U.S. market.