Top Interview Questions

📝 Introduction

Are you preparing for a data engineering or cloud computing interview in 2025? One of the most in-demand AWS services is AWS Glue, widely used for ETL (Extract, Transform, Load), big data analytics, and data pipelines. In this guide, we bring you the Top 25 AWS Glue Interview Questions and Answers with Examples (2025 Updated) to help you crack your next technical round with confidence.

From Glue ETL, Glue Studio, Dynamic Frames, schema handling, job bookmarks, and pricing to real-world ETL use cases, these top interview questions cover everything you need as a fresher or an experienced professional.

If you’re also preparing for broader cloud and data engineering roles, check out our related posts on Top 25 Solution Architect Interview Questions and Answers and AWS Interview Questions and Answers for 2025 – Freshers & Experienced to strengthen your overall knowledge.

Let’s dive into these AWS Glue interview questions with examples and simplify complex concepts in an easy-to-understand way!

1. What is AWS Glue?

Answer: AWS Glue is a fully managed, serverless ETL (Extract, Transform, Load) service by Amazon Web Services. Fully managed means AWS handles the setup, scaling, and maintenance. Serverless means you don’t manage servers; AWS runs your jobs automatically, and you pay only for what you use.

Explanation: Think of AWS Glue like a data kitchen. Your raw data is the ingredients (from different sources), Glue cleans and prepares them (transform), and then serves them in a ready-to-use form (load). This helps in preparing data for analytics, reporting, or machine learning.

Examples:

1. An e-commerce site collects customer orders in MySQL. Glue extracts the data, removes duplicates, and loads it into Amazon Redshift for analytics.
   
   
2. A log processing system extracts raw logs from S3, filters errors, and loads the cleaned data into AWS Athena for queries.
   
   

2. What does ETL mean in AWS Glue?

Answer: ETL stands for Extract, Transform, Load – the main process used in AWS Glue to prepare and move data.

Explanation:

• Extract – Pull data from various sources like databases, S3, or APIs.
  
  
• Transform – Clean, enrich, and format data to match the target requirements.
  
  
• Load – Store the transformed data in destinations like Amazon Redshift, S3, or Snowflake.
  
  

It’s like making juice: pick fruits (extract), clean and blend them (transform), and pour into a glass (load).

Examples:

1. Extract customer feedback from S3, remove null values, and load into DynamoDB for dashboard display.
   
   
2. Extract IoT device logs, change time format, and load into a data warehouse for analytics.
   
   

3. What is AWS Glue Data Catalog?

Answer: The AWS Glue Data Catalog is a central metadata repository that stores information (schemas, table definitions, data locations) about your data.

Explanation: Think of it as a library catalog – it doesn’t store the books (data) but tells you where each book is and what’s inside (schema). It allows other AWS services like Athena and Redshift Spectrum to query the data easily.

Examples:

1. Store metadata about CSV files in S3 so Athena can run SQL queries without moving the data.
   
   
2. Maintain schema details for Parquet files used by multiple analytics teams.
   
   

4. What are Crawlers in AWS Glue?

Answer: A crawler is an AWS Glue feature that scans your data sources (like S3, databases), detects the schema, and updates the Data Catalog automatically.

Explanation: Instead of manually defining schemas, crawlers analyze your files, determine column names, data types, and partitions, and save this info in the Data Catalog.

Examples:

1. A crawler scans a folder in S3 containing JSON logs and creates a table in the Data Catalog.
   
   
2. Crawl an RDS PostgreSQL database to detect table structures for ETL processing.
   
   

5. What is a Glue Job?

Answer: A Glue job is a task that performs ETL operations using Python or Scala scripts on AWS Glue.

Explanation: When you want to process data (extract → transform → load), you create a Glue job that runs your code on AWS-managed servers.

Examples:

1. A job that converts CSV files in S3 to Parquet format.
   
   
2. A job that joins sales data from MySQL with customer data from DynamoDB and loads the result to Redshift.
   
   

6. What is a Glue Trigger?

Answer: A Glue trigger is a scheduler or event-based activator that starts Glue jobs or workflows.

Explanation: Think of a trigger like an alarm clock — it decides when your ETL job should run. Triggers can start jobs:

• On a schedule (e.g., every day at 2 AM)
  
  
• When another job finishes
  
  
• When new data appears in S3
  
  

Examples:

1. A trigger runs a Glue job every night at midnight to process sales reports.
   
   
2. A trigger starts a job whenever new CSV files are uploaded to an S3 bucket.
   
   

7. What is a Glue Workflow?

Answer: A workflow in AWS Glue is a collection of jobs and triggers that run in a sequence to complete a larger ETL process.

Explanation: Instead of running jobs separately, you can chain them together in a workflow. This helps when one job’s output is another job’s input.

Examples:

1. Workflow to extract from MySQL → transform in Glue → load to Redshift.
   
   
2. Workflow to crawl new S3 data → run data cleaning job → send data to Athena.
   
   

8. What is the difference between Glue ETL and Glue Studio?

Answer:

• Glue ETL: Traditional way of writing Python/Scala scripts to transform data.
  
  
• Glue Studio: Visual, drag-and-drop interface to design ETL jobs without coding.
  
  

Explanation: Glue ETL is great for developers who like coding, while Glue Studio is better for beginners or quick jobs.

Examples:

1. The developer writes Python code to merge datasets in Glue ETL.
   
   
2. Business analyst uses Glue Studio’s UI to clean data without touching code.
   
   

🔑 Key Differences (Comparison Table)

9. What is AWS Glue Streaming ETL?

Answer: Streaming ETL in AWS Glue processes real-time incoming data instead of batch data.

Explanation: Instead of waiting for data to be stored, Glue Streaming can process messages from sources like Kinesis Data Streams or Kafka immediately.

Examples:

1. Streaming Glue job processes live sensor data and stores results in S3.
   
   
2. Process live clickstream data from a website for instant analytics.
   
   

10. What are Dynamic Frames in AWS Glue?

Answer: Dynamic Frames are AWS Glue’s version of DataFrames that can handle semi-structured data (like JSON) and easily adapt to schema changes.

Explanation: Unlike Spark DataFrames, Dynamic Frames automatically handle schema inconsistencies and allow easier data cleaning.

Examples:

1. Read JSON files from S3 into a Dynamic Frame and fix missing fields before saving.
   
   
2. Merge CSV and JSON data without worrying about exact column matching.
   
   

11. What is the difference between Dynamic Frame and DataFrame in Glue?

Answer:

• Dynamic Frame: Flexible, handles schema changes, supports AWS Glue transformations.
  
  
• DataFrame: Spark’s standard, strict schema handling, faster performance for fixed schemas.
  
  

Examples:

1. Use Dynamic Frame for logs with different formats each day.
   
   
2. Use DataFrame for a fixed product catalog dataset.
   
   

🔑 Key Differences (DynamicFrame vs DataFrame)

12. What are Glue Connections?

Answer: Glue connections are configurations to connect to external data sources like RDS, Redshift, or other JDBC databases.

Explanation: It stores authentication details (host, username, password) so Glue can access your source without hardcoding credentials.

Examples:

1. Connect to an RDS MySQL database to extract sales data.
   
   
2. Connect to a PostgreSQL database for ETL processing.
   
   

13. What are Glue Development Endpoints?

Answer: A development endpoint is an interactive environment where you can write and test ETL scripts before deploying them as Glue jobs.

Explanation: It allows you to connect Jupyter Notebooks to Glue for debugging and experimenting.

Examples:

1. Test a Python script in a dev endpoint before running it on production.
   
   
2. Try different transformations on a small data sample.
   
   

14. How does AWS Glue integrate with Amazon Athena?

Answer: Glue and Athena work together through the Glue Data Catalog — Athena uses it to understand the schema of your data.

Explanation: You can run SQL queries on raw S3 data without moving it, thanks to the Data Catalog.

Examples:

1. Use Glue crawler to detect S3 schema → run Athena query instantly.
   
   
2. Store Parquet files in S3 and query them with Athena for reporting.
   
   

15. How is Glue different from AWS Data Pipeline?

Answer:

• Glue: Serverless, code-based or visual ETL, built for analytics.
  
  
• Data Pipeline: Workflow automation service, can run EC2 or EMR jobs.
  
  

Examples:

1. Use Glue to transform and load sales data into Redshift.
   
   
2. Use Data Pipeline to schedule a Hadoop job on EMR.
   
   

🔑 Key Differences (Glue vs Data Pipeline)

16. What is AWS Glue Studio Visual ETL?

Answer: AWS Glue Studio is a visual interface inside AWS Glue that lets you build, run, and monitor ETL (Extract, Transform, Load) jobs without writing complex Spark code.

It provides a drag-and-drop editor where you can connect data sources, apply transformations, and load them into targets.

Explanation

• Traditionally, Glue ETL required writing PySpark or Scala scripts, which was developer-heavy.
  
  
• Glue Studio makes ETL low-code / no-code by automatically generating the Spark code behind the scenes.
  
  
• It supports:
  ✅ Data sources: S3, RDS, Redshift, DynamoDB, etc.
  ✅ Transformations: filter, join, apply mapping, resolve choice, aggregate.
  ✅ Destinations: Redshift, S3, Snowflake, etc.
  
  
• You can still edit the generated code if you want custom logic.

Examples:

Suppose you are a data analyst and you need to:

1. Load sales data from S3 (CSV).
   
   
2. Join it with customer data from RDS (MySQL).
   
   
3. Clean null values.
   
   
4. Load the final dataset into Amazon Redshift for BI reporting.
   
   

✅ In Glue Studio Visual ETL:

• You drag “S3 Source” → “RDS Source” → “Join” → “Drop Null Fields” → “Redshift Target”.
  
  
• Glue Studio generates a Spark script and runs it for you.
  
  
• You can monitor the job visually in the console.

17. What is AWS Glue Bookmarking?

Answer: AWS Glue Bookmarking is a feature that allows ETL jobs to track the state of processed data so that on the next run, the job only processes new or changed data instead of reprocessing everything.

Explanation

• In ETL, data often lands in batches (daily logs, new CSV files, etc.).
  
  
• Without bookmarking → every time the Glue job runs, it reprocesses all files, wasting time and cost.
  
  
• With bookmarking enabled → Glue remembers which files or partitions it has already processed.
  
  
• It stores this “bookmark” in Glue’s metadata and uses it for the next job run.
  
  

This makes Glue incremental and avoids duplication.

Examples:

1. Process only yesterday’s sales instead of reprocessing old sales records.
   
   
2. Process only new log files uploaded to S3.
   
   

18. What is AWS Glue Job Bookmark State?

Answer: The Job Bookmark State in AWS Glue is the stored metadata that tracks what data a job has already processed. It tells Glue where to “resume” the next time the job runs, preventing duplicate processing of the same data.

Explanation

• Every Glue job can maintain a bookmark state.
  
  
• This state is saved in AWS Glue’s Data Catalog (metadata store).
  
  
• It includes information like:
  
  
  • Which files/partitions from S3 were processed.
    
    
  • Which rows from a database table were read (using primary keys or timestamps).
    
    
  • The last checkpoint for streaming data sources.
    
    
• When the job runs again, Glue looks at the bookmark state and starts from where it left off.
  
  

So essentially → Job Bookmark State = Glue’s memory of past runs.

Examples:

1. Keeps a marker for the last processed date in a transaction table.
   
   
2. Saves the last read position in a Kafka stream.
   
   

19. How does Glue handle schema changes?

Answer: Schema change means when the structure of the data (columns, data types, or order) changes over time in a source system (like S3, RDS, DynamoDB, etc.). For example:

• • A new column is added.
    
    
  • A column is removed.
    
    
  • A column’s data type changes.
    
    

AWS Glue provides schema evolution support to automatically detect, adapt, and process such changes without failing jobs.

Explanation

Glue handles schema changes in two main layers:

1. Glue Crawlers (Schema Detection):
   
   
   • Crawlers automatically scan the data source and update the schema in the Glue Data Catalog.
     
     
   • If a new column is added, Glue crawler updates the table schema.
     
     
   • If a column is removed or renamed, Glue updates the schema accordingly.
     
     
2. DynamicFrames (ETL Processing):
   
   
   • Unlike Spark DataFrames, DynamicFrames are schema-flexible.
     
     
   • They allow you to work with semi-structured or evolving data.
     
     
   • DynamicFrames can handle missing fields, nulls, and different data types gracefully.
     
     
   • You can also convert DynamicFrames to DataFrames if strict schema is needed.

Examples:

1. 1. A new column “discount” appears in sales CSV — Glue updates the schema.
      
      
   2. JSON logs get a new field — crawler detects and adds it to the catalog.
      
      

20. What are Glue Transformations?

Answer: In AWS Glue, transformations are operations that allow you to modify, clean, restructure, or enrich your data while moving it from a source (like S3, RDS, DynamoDB) to a target (like Redshift, S3, or another database).

They are applied mainly on DynamicFrames (Glue’s schema-flexible data structure), but can also be used on DataFrames after conversion.

Explanation

• Transformations are similar to SQL operations like SELECT, JOIN, GROUP BY, or WHERE, but they are applied programmatically using PySpark inside AWS Glue.
  
  
• They help in data cleaning, normalization, enrichment, and reformatting before storing it in the target.
  
  
• Example use cases:
  
  
  • Filtering out null or duplicate records.
    
    
  • Joining two datasets.
    
    
  • Changing column data types.
    
    
  • Flattening nested JSON.
    
    
  • Resolving schema conflicts.

Examples:

1. 1. Drop duplicates from customer data.
      
      
   2. Join orders and customers tables by customer_id.
      
      

21. How do you schedule Glue jobs?

Answer: In AWS Glue, scheduling a job means running it automatically at a defined time or event instead of manually starting it. This is useful for automating ETL (Extract, Transform, Load) pipelines — e.g., refreshing data every night at midnight or after a file arrives in S3.

Explanation

You can schedule Glue jobs using three main approaches:

• Using AWS Glue Triggers
  
  

1. 1. • Triggers allow you to start jobs on a schedule or based on an event.
        
        
      • Types of triggers:
        
        
        • Scheduled Trigger → runs at specific times (cron expressions).
          
          
        • Event-based Trigger → runs when another Glue job completes.
          
          

• • • On-demand Trigger → runs only when started manually.
      
      

• Using AWS Glue Workflows
  
  

• • • A workflow helps schedule and manage multiple jobs together.
      
      
    • Example: Job A (load data) → Job B (clean data) → Job C (store in Redshift).
      
      
    • Each job runs in sequence with scheduling built in.
      
      

• Using External Services (CloudWatch Events / EventBridge)
  
  

• • • You can also use Amazon EventBridge (formerly CloudWatch Events) to run Glue jobs at specific intervals.
      
      
    • This method gives more flexibility (e.g., trigger Glue job when a new file is uploaded to S3).

Examples:

1. 1. Run every night at 1 AM to refresh daily sales.
      
      
   2. Run every 5 minutes for near real-time analytics.
      
      

22. What is the difference between Glue 1.0, 2.0, and 3.0?

Answer: They are versions of Glue Spark runtime — newer versions bring faster execution, more features, and cost efficiency.

Examples:

1. 1. Glue 3.0 supports Python 3.9 and Spark 3.1.
      
      
   2. Glue 1.0 runs slower and has older library versions.
      
      

🔑 Comparison Table

23. How does Glue work with Redshift?

Answer: Glue acts as a bridge between raw data sources (S3, RDS, DynamoDB, etc.) and Redshift. It extracts, cleans, transforms, and then loads data into Redshift tables so analysts and BI tools can query it.

AWS Glue is a serverless ETL (Extract, Transform, Load) service.

Amazon Redshift is a fully managed data warehouse used for analytics.

Explanation

Glue works with Redshift in two main ways:

1. ETL (Extract-Transform-Load) Jobs → Redshift
   
   
   • Glue reads raw data (from S3, DynamoDB, etc.).
     
     
   • Applies transformations (cleaning, schema changes, joins).
     
     
   • Loads the transformed data into Redshift tables.
     
     
   • Uses Redshift JDBC connection or the COPY command (optimized for large loads).
     
     
2. Glue Crawlers + Data Catalog → Redshift Spectrum
   
   
   • Glue Crawlers scan Redshift tables and update the Glue Data Catalog.
     
     
   • Redshift Spectrum can then query data in S3 + Redshift together, using a single schema.

Examples:

1. 1. Load transformed CSV from S3 into Redshift tables.
      
      
   2. Export Redshift table data for cleaning in Glue and reload it.
      
      

24. How is Glue priced?

Answer: AWS Glue is a serverless ETL service, so you only pay for what you use.
Pricing is based mainly on:

1. 1. Data Processing Units (DPUs) for running ETL jobs and development endpoints.
      
      
   2. Crawlers and Data Catalog storage.
      
      
   3. Data transfer and requests (minor costs).

Examples:

• • ETL Job: 2 DPUs for 1 hour → 2 DPU-hours billed.
    
    
  • Crawler: 1 DPU for 15 minutes → 0.25 DPU-hours billed.
    
    
  • Catalog Storage: 800K objects → Free.
    
    
  • Catalog Requests: 700K requests → Free.
    
    

👉 Your total monthly bill = charges for 2.25 DPU-hours only.

25. What are common use cases of AWS Glue?

Answer: AWS Glue is used for:

• • ETL pipelines
    
    
  • Data lakes
    
    
  • Metadata management
    
    
  • Batch & streaming integration
    
    
  • ML data prep
    
    
  • Database migration
    
    
  • Data compliance

✅ Conclusion

Mastering AWS Glue concepts such as ETL workflows, schema evolution, DynamicFrames vs. DataFrames, Glue transformations, and integrations with Redshift is crucial for anyone aiming for success in data engineering, cloud development, or big data analytics roles.

This collection of the Top 25 AWS Glue Interview Questions and Answers with Examples (2025) ensures you are fully prepared to answer both technical and scenario-based questions. These top interview questions not only build your confidence but also showcase your expertise in serverless data integration and automation with AWS Glue.

👉 For complete preparation, don’t miss our guides on Top 25 Python, NumPy & Pandas Interview Questions and Top 25 Generative AI Interview Questions & Answers (2025) to sharpen your AI/ML and coding skills alongside cloud expertise.

With the right preparation and practice, you can confidently clear your AWS Glue interview and move a step closer to your data engineering career goals. 🚀