Databricks Interview Questions

1. What is Databricks?

Databricks is a powerful cloud-based data intelligence platform that revolutionizes data management by combining data warehousing and data lakes into a single, seamless Data Lakehouse architecture. Created by the original developers of Apache Spark, Databricks empowers data teams to handle massive datasets, build efficient ETL pipelines, execute fast SQL queries, and deploy AI and machine learning models in a collaborative environment.

As a unified open analytics platform, Databricks simplifies building, deploying, sharing, and scaling enterprise-grade data analytics and AI solutions. It seamlessly integrates with your cloud storage and security, while automatically managing and provisioning cloud infrastructure—letting you focus on insights, not ops.

Key Benefits of Databricks Data Lakehouse:

• Scalable Processing: Tackle big data with Apache Spark at its core.
• Collaborative Workflows: Ideal for data engineers, analysts, and ML experts.
• Cost-Effective: Pay only for what you use in your preferred cloud (AWS, Azure, GCP).

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2. Key Features of Databricks?

Key Features of Databricks

1. Unified data and AI platform – Databricks combines data engineering, data science, and machine learning in a single cloud‑native environment built on Apache Spark.
2. Collaborative notebooks – Supports interactive notebooks with Python, SQL, Scala, and R, enabling teams to code, visualize, and share analyses in real time.
3. Autoscaling clusters – Dynamically scales compute clusters up or down based on workload, improving performance while reducing cloud cost.
4. Delta Lake integration – Provides ACID transactions, schema enforcement, and time‑travel on data lakes, making ETL pipelines reliable and production‑ready.
5. Batch and streaming in one engine – Uses Spark Structured Streaming for both batch and real‑time processing on the same codebase, simplifying pipeline design.
6. MLflow‑based MLOps – Native MLflow integration helps track experiments, manage models, and deploy ML models into production seamlessly.
7. Job scheduling and orchestration – Built‑in Jobs UI lets you schedule, parameterize, and monitor recurring Databricks workloads, similar to traditional ETL orchestration tools.
8. Multi‑language and library support – Works with major data and ML libraries (pandas, scikit‑learn, TensorFlow, PyTorch, Spark SQL, etc.), making it flexible for data teams.
9. Cloud‑native and multi‑cloud – Runs on AWS, Azure, and GCP, with deep integration into cloud storage (S3, ADLS, GCS) and identity systems. [in.indeed]
10. Governance and security – Offers role‑based access control (RBAC), data lineage, and audit logs, helping enterprises meet compliance and data‑governance requirements.

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3. What is Apache Spark?

• Apache Spark is a fast, open-source tool used to process large amounts of data (big data).
• It helps companies analyze data quickly, even when the data is too big for normal systems.
• Spark is widely used in data engineering, data analytics, and machine learning.
• Very Fast – Processes data in memory (RAM), not just from disk
• Handles Big Data Easily – Can work with huge datasets
• Supports Real-Time Processing – Not just batch data
• Machine Learning Support – Built-in ML libraries

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4. What is Delta Lake, and how does it improve data reliability?

• Delta Lake is an open-source storage layer that brings reliability to data lakes.
• It provides ACID transactions, scalable metadata handling.
• Unifies streaming and batch data processing.
• Delta Lake allows for versioned data, which means you can easily time travel to previous versions of data.

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5. What are the best performance tuning techniques in Databricks?

• Choose columnar formats like Parquet or Delta for Spark optimization by reducing I/O and by scanning only required columns, unlike row-oriented CSV.
• Leverage lazy evaluation in Spark Databricks to build optimized DAGs before actions trigger jobs.
• Understand narrow vs wide transformations for Spark in Databricks: Narrow avoids shuffles (one stage), while wide requires repartitioning.
• Tune spark.sql.shuffle.partitions based on data size (default 200) to minimize overhead in Databricks Spark jobs.
• Enable Catalyst Optimizer with spark.sql.cbo.enabled for cost-based join reordering in Databricks, pushing filters before joins.

Advanced Spark Features

• Activate Adaptive Query Execution (AQE) in Spark 3.2+ Databricks (spark.sql.adaptive.enabled=true) to dynamically coalesce small partitions post.
• Apply predicate pushdown automatically in Databricks Spark reads from sources like SQL Server, filtering at the source to cut data transfer.
• Cache DataFrames strategically only for reused data in Databricks (df.cache() then action), avoiding memory waste.
• Set initial partitions to 128MB-1GB via df.rdd.getNumPartitions() for balanced parallelism in Databricks clusters, optimizing for core count in performance interviews.

Join Strategies Mastery

• Prioritize broadcast joins for small tables (<10MB default, tunable via spark.sql.autoBroadcastJoinThreshold) in Databricks Spark, bypassing shuffles.

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6. What is lazy evaluation in Apache Spark, and why does it matter?

Lazy evaluation means Spark doesn’t execute transformations immediately. Instead, it builds a logical execution plan and only runs it when an action (like count() or show()) is triggered.

This approach allows Spark to optimize the execution plan, often combining multiple transformations into fewer stages for better efficiency.

Example: A chain like filter → select → groupBy won’t run until an action is called, allowing Spark to optimize the entire workflow before execution.

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7. How do you process large datasets from Azure Data Lake Storage (ADLS) using Databricks?

Key Steps for Databricks Streaming Pipelines

• Configure Databricks cluster for streaming: Select Databricks Runtime with Apache Spark Structured Streaming optimized for real-time data ingestion and low-latency processing.-
• Ingest from Kafka/Event Hubs: Use spark.readStream.format("kafka") in Databricks notebooks to pull real-time data streams from Apache Kafka or Azure Event Hubs.-
• Apply real-time transformations: Leverage DataFrame APIs for filtering, aggregations, and joins in Databricks Structured Streaming—processes data continuously without batching.-
• Output to Delta Lake: Write streams with writeStream.format("delta").outputMode("append") to Delta Lake tables for ACID-compliant, scalable real-time analytics in Databricks.-
• Monitor & auto-scale: Track via Databricks Ganglia metrics and Spark UI streaming tab; enable Databricks autoscaling clusters for dynamic real-time pipeline performance.

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8. What is the difference between batch and streaming data processing in Spark?

In Apache Spark, data can be processed in two main ways depending on how and when it is handled:

Batch Processing: This method processes large volumes of data at scheduled intervals. Data is collected over time and then analyzed as a whole. It’s commonly used for tasks like generating daily or weekly reports.

Streaming Processing: This approach handles data in real-time as it is generated. Instead of waiting, the system processes incoming data continuously. making it ideal for use cases like fraud detection, live dashboards, and real-time analytics.

Batch is ideal for historical analysis
while streaming is best for real-time insights.

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9. How do you merge large datasets with different schemas in Databricks?

To merge large datasets with different schemas in Databricks, the simplest idea is:

Use MERGE INTO with schema evolution (Delta Lake) If your target is a Delta table and you want to update/insert rows from a source that has extra or different columns, Databricks supports MERGE with schema evolution:

MERGE WITH SCHEMA EVOLUTION INTO target_table USING source_table
ON source_table.id = target_table.id
WHEN MATCHED THEN
  UPDATE SET *
WHEN NOT MATCHED THEN
  INSERT *
WHEN NOT MATCHED BY SOURCE THEN
  DELETE;

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10. What are the key features of Databricks Runtime?

Core Features:

Photon Engine - A high-performance, Databricks-native vectorized query engine that accelerates SQL workloads, DataFrames, and feature engineering without requiring code changes

Performance Optimizations - Includes infrastructure improvements like autoscaling, faster data processing for aggregations and joins, and enhanced disk cache access

Enhanced Delta & Parquet Operations - Accelerated writing for UPDATE, DELETE, MERGE INTO, INSERT, and CREATE TABLE AS SELECT operations, including support for wide tables with thousands of columns

Structured Streaming - Near real-time processing engine for streaming data workloads

Reliability & Security - Improved usability, performance, and security enhancements over standard Apache Spark

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11. What is Adaptive Query Execution (AQE) in Databricks?

AQE is a runtime optimization feature that dynamically adjusts query plans based on real-time metric.

It can:

• Optimize join strategies
• Adjust shuffle partitions
• Improve overall query performance

This makes Spark jobs more efficient without manual tuning.

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12. How do you ensure data quality and integrity in Databricks?

Maintaining data quality involves:

• Using Delta Lake for ACID transactions and schema enforcement
• Implementing validation checks in pipelines
• Monitoring and logging errors

These practices help ensure reliable and consistent data processing.

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13. How do you troubleshoot memory issues in Spark jobs on Databricks?

1. Check Spark UI:

• Use the Spark UI in Databricks to monitor stages, tasks, and memory usage
• Look for issues like skewed tasks, long-running stages, or frequent garbage collection

2. Optimize Data Partitioning:

• Avoid too few or too many partitions
• Use repartition() or coalesce() wisely to balance workload across nodes

3. Cache Carefully:

• Cache only the data you reuse multiple times
• Unpersist unused DataFrames to free memory

4. Tune Spark Configurations:

• Adjust settings like:
• spark.executor.memory
• spark.driver.memory
• spark.sql.shuffle.partitions
• Increase memory only when necessary and based on workload

5. Avoid Data Skew

• Skewed data can overload a single executor
• Use techniques like salting or filtering to distribute data evenly

6. Optimize Joins

• Prefer broadcast joins for small datasets
• Avoid large shuffles when joining big tables

7. Reduce Data Size

• Select only required columns
• Filter data early to minimize memory usage

8. Use Efficient File Formats

• Use formats like Parquet instead of CSV for better compression and performance
• In simple terms: Monitor your job, balance the workload, reduce unnecessary data, and tune configurations to prevent memory overload in Spark.

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14. What are the best practices for data governance in Databricks?

Effective data governance includes:

Implement Fine-Grained Access Control Use role-based access control (RBAC) to restrict who can view, modify, or manage data at different levels (tables, columns, etc.).

Use a Centralized Data Catalog Leverage tools like Unity Catalog to manage metadata, track data lineage, and maintain a single source of truth.

Enable Data Auditing & Logging Track user activities and data access to ensure compliance and quickly identify any unauthorized actions.

Apply Data Classification & Tagging Label sensitive data (PII, financial data, etc.) to apply appropriate security and compliance policies.

Maintain Data Quality & Versioning Use validation checks and version control (like Delta tables) to ensure data accuracy, consistency, and traceability.

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15. How do you read a CSV file in Databricks using Spark?

df = spark.read.format("csv") \
    .option("header", "true") \
    .option("inferSchema", "true") \
    .load("/FileStore/data/sample.csv")

df.show()

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16. How do you create a temporary view and run SQL queries?

df.createOrReplaceTempView("people")

result = spark.sql("SELECT * FROM people WHERE age > 30")
result.show()

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17. How do you run machine learning workflows in Databricks?

1. Prepare and clean data
2. Perform feature engineering
3. Train models using MLlib or frameworks like TensorFlow
4. Evaluate model performance
5. Track and deploy using MLflow

Databricks simplifies end-to-end ML pipelines at scale.

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18. How do you write a DataFrame to a Parquet file?

df.write.format("parquet") \
    .mode("overwrite") \
    .save("/FileStore/output/data.parquet")

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19. How does Databricks handle job scheduling and orchestration?

Databricks provides built-in job scheduling features:

• Schedule notebooks or scripts
• Use job clusters for cost efficiency
• Define task dependencies for complex workflows

This makes automation and orchestration straightforward.

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20. How do teams collaborate and share data in Databricks?

• Organize workspaces by team or project
• Use RBAC for access control
• Share notebooks and datasets securely
• Use data catalogs for discovery