Best 40+ AWS Glue Interview Questions and Answers for Freshers & Experienced 2026

TL;DR

1. AWS Glue interview questions usually focus on ETL processes, data integration, AWS Glue components, PySpark transformations, performance optimization, and real-world data engineering scenarios.
2. Freshers often get questions about Glue Crawlers, Data Catalog, Jobs, and Triggers.
3. Experienced professionals face questions on DynamicFrames, Job Bookmarks, partitioning, schema evolution, optimization techniques, and large-scale ETL architectures.
4. To help you prepare effectively, we’ve compiled more than 42 AWS Glue interview questions ranging from basic concepts to advanced implementation scenarios. 
5. It helps prepare for roles such as Data Engineer, AWS Engineer, and Cloud Data Developer.

What Is AWS Glue?

AWS Glue is a serverless data integration service that assists organizations in discovering, preparing, transforming, and loading data for analytics. It simplifies ETL workflows by removing the need for infrastructure management and automatically adjusting resources based on workload demands.

Data engineers commonly use AWS Glue to transfer and transform data between Amazon S3, Amazon Redshift, Amazon RDS, and other AWS analytics services. Because AWS Glue fits well into modern cloud data architectures, it frequently comes up in AWS and Data Engineering interviews.

Want to build practical cloud and data engineering skills through hands-on projects? Check out HCL GUVI’s AWS and Cloud Computing programs that cover AWS services, ETL pipelines, data engineering concepts, and real-world project implementation.

Why Are AWS Glue Skills in Demand?

Organizations are generating more data than ever. Industry reports show that companies are investing heavily in cloud-based analytics and data engineering platforms. This trend creates a strong need for professionals who can build scalable ETL pipelines.

AWS Glue is essential in many modern data lakes because it automates metadata discovery, schema management, and data transformation workflows. Companies looking for Data Engineers often expect candidates to know AWS Glue, along with services such as Amazon S3, Athena, Redshift, and Lake Formation.

AWS Glue Interview Questions for Freshers

1. What is AWS Glue?

AWS Glue is a fully managed serverless ETL service that helps discover, catalog, transform, and move data for analytics and machine learning workloads.

2. What are the main components of AWS Glue?

The primary components include:

• Data Catalog
• Crawlers
• ETL Jobs
• Triggers
• Workflows
• Glue Studio
• Connections

3. What is a Glue Crawler?

A Glue Crawler scans data sources, finds schemas, and automatically creates metadata tables in the AWS Glue Data Catalog.

4. What is the AWS Glue Data Catalog?

The Data Catalog is a centralized metadata repository that stores information about datasets, schemas, partitions, and data locations.

5. What is an ETL Job in AWS Glue?

An ETL Job extracts data from source systems, transforms it based on business needs, and loads it into a target location.

6. Which programming languages are supported by AWS Glue?

AWS Glue primarily supports:

• Python
• PySpark
• Scala
• Spark SQL

7. What is AWS Glue Studio?

AWS Glue Studio is a visual interface that lets developers create, monitor, and manage ETL pipelines with minimal coding.

8. What is a Trigger in AWS Glue?

A Trigger initiates ETL jobs based on schedules, events, or the successful completion of other jobs.

9. What is the difference between a Crawler and a Job?

A Crawler discovers metadata and updates the Data Catalog, while a Job performs data transformation and movement.

10. Why is AWS Glue considered serverless?

AWS manages the underlying infrastructure on its own, allowing developers to focus only on data processing logic.

11. What is schema inference?

Schema inference is the process of automatically identifying column names, data types, and structures from source data.

12. What is partitioning in AWS Glue?

Partitioning organizes data into logical segments, which improves query performance and lowers processing costs.

13. What is Amazon S3’s role in AWS Glue?

Amazon S3 often acts as the storage layer for source files, transformed datasets, and data lake architectures.

14. What is a Connection in AWS Glue?

A Connection stores network and authentication details needed to access external data sources.

15. Can AWS Glue connect to databases?

Yes. AWS Glue supports databases such as MySQL, PostgreSQL, Oracle, SQL Server, and Amazon RDS.

AWS Glue Interview Questions for Experienced Professionals

16. What is the difference between DynamicFrame and DataFrame?

DynamicFrames are AWS Glue-specific structures made for semi-structured data and schema flexibility. DataFrames are Apache Spark structures optimized for performance and Spark tasks.

17. What are Job Bookmarks?

Job Bookmarks track previously processed data, allowing AWS Glue to process only new records during future runs.

18. What is schema evolution?

Schema evolution lets data pipelines handle changes in source schemas without disrupting downstream processes.

19. How does AWS Glue handle incremental data processing?

AWS Glue typically uses Job Bookmarks, timestamps, partition filtering, and change tracking to process only newly added records.

20. What is Pushdown Predicate Optimization?

Pushdown predicates filter data before reading it into Spark, cutting down I/O operations and improving performance.

21. What are Glue Workflows?

Glue Workflows manage multiple jobs, crawlers, and triggers into a single end-to-end pipeline.

22. What is AWS Glue Schema Registry?

Schema Registry helps manage and validate schemas used in streaming applications and event-driven architectures.

23. How do you optimize Glue Job performance?

Common optimization techniques include:

• Using Parquet instead of CSV
• Applying partition pruning
• Reducing data shuffles
• Filtering early
• Right-sizing DPUs

24. What are DPUs in AWS Glue?

DPU stands for Data Processing Unit. It represents a defined combination of memory and compute resources assigned to a Glue job.

25. How do you handle failed jobs?

You can manage failed jobs using retries, CloudWatch monitoring, error logging, workflow dependencies, and alerting tools.

26. What is partition pruning?

Partition pruning allows AWS Glue to scan only the relevant partitions instead of the entire dataset.

27. What is data skew in Spark?

Data skew happens when some partitions have a lot more data than others, causing processing delays.

28. How does AWS Glue integrate with Athena?

The AWS Glue Data Catalog acts as the metadata layer that Athena uses to find and query datasets.

29. Why is Parquet preferred over CSV?

Parquet is a columnar storage format that offers better compression, faster queries, and lower storage costs.

30. How does Glue integrate with Redshift?

AWS Glue can load transformed data into Amazon Redshift using JDBC connections and efficient bulk loading methods.

Scenario-Based AWS Glue Interview Questions

31. How would you process only newly arrived files in Amazon S3?

Job Bookmarks, timestamp filtering, and partition-based ingestion strategies can help avoid reprocessing older files.

32. A Glue Job is taking too long. What would you investigate?

Investigation starts from:

• Data volume
• Partitioning strategy
• Spark shuffles
• DPU allocation
• File formats
• Predicate pushdown opportunities

33. How would you handle duplicate records?

You can implement deduplication logic using primary keys, Spark transformations, or merge operations before loading data.

34. How would you migrate CSV pipelines to Parquet?

Create a transformation job that reads CSV data, applies validation rules, and writes the output in Parquet format.

35. How would you handle schema changes from source systems?

Using schema evolution techniques, validation layers, and automated catalog updates can help maintain pipeline stability.

36. How would you design a daily ETL pipeline?

A typical solution includes:

• S3 ingestion
• Glue Crawler execution
• ETL transformation job
• Data quality checks
• Redshift loading
• Monitoring and alerts

37. How would you secure sensitive data?

Encryption, IAM policies, Lake Formation permissions, and network controls can help secure AWS Glue workloads.

AWS Glue Performance and Optimization Questions

38. How can you reduce AWS Glue costs?

Costs can be reduced by:

• Processing incremental data
• Using partitioned datasets
• Optimizing job duration
• Choosing efficient file formats
• Avoiding unnecessary crawler runs

39. Why are small files a problem?

Having many small files increases metadata overhead and decreases Spark processing efficiency.

40. What is partition projection?

Partition projection decreases the need to manually maintain partition metadata and improves query efficiency.

41. Why should transformations occur early in the pipeline?

Early filtering reduces the amount of data processed later, improving performance and lowering costs.

42. How would you monitor AWS Glue jobs?

AWS CloudWatch metrics, logs, alarms, and Glue monitoring dashboards offer insight into job health and performance.

Real World Example of AWS Glue in Action

Consider an e-commerce company that collects customer orders, website activity, and payment information in Amazon S3.

AWS Glue Crawlers automatically discover new datasets and update the Data Catalog. ETL Jobs transform raw transaction data into formats ready for analysis. The processed data is loaded into Amazon Redshift, where business analysts generate reports on customer behavior, revenue trends, and inventory forecasting.

A similar architecture is often used by retail, fintech, healthcare, and media organizations that operate large-scale data lakes.

Common Mistakes During AWS Glue Interviews

Confusing Crawlers with Jobs – Crawlers discover metadata, while Jobs perform transformations. Clearly explain the difference.

Ignoring DynamicFrames – Many candidates only discuss DataFrames. Interviewers often expect knowledge of both structures.

Overlooking Job Bookmarks – Incremental processing is a common interview topic, and Job Bookmarks are often part of the solution.

Not Understanding Partitioning – Partitioning directly impacts performance and cost savings.

Skipping Real-World Scenarios – Experienced candidates should explain practical implementations instead of only theoretical concepts.

Want to build practical cloud and data engineering skills through hands-on projects? Check out HCL GUVI’s AWS and Cloud Computing programs that cover AWS services, ETL pipelines, data engineering concepts, and real-world project implementation.

Conclusion

AWS Glue interview questions often assess both basic ETL knowledge and real-world data engineering experience. Freshers should focus on core components like Crawlers, Jobs, Triggers, and the Data Catalog, while experienced professionals should be ready to discuss optimization, schema evolution, DynamicFrames, and large-scale pipeline design.

Mastering these topics will boost your confidence in AWS Data Engineering interviews and help you create scalable cloud data solutions. A practical next step is to gain hands-on experience by developing end-to-end AWS Glue projects using Amazon S3, Athena, and Redshift.

FAQs

1. Is AWS Glue important for Data Engineer interviews?

Yes. AWS Glue is often used in cloud-based ETL pipelines and is frequently discussed in AWS and Data Engineering interviews.

2. What are the most important AWS Glue topics for freshers?

Freshers should focus on Crawlers, Data Catalog, ETL Jobs, Triggers, Glue Studio, and AWS Glue architecture.

3. What advanced AWS Glue topics are asked in interviews?

Experienced candidates are often asked about DynamicFrames, Job Bookmarks, schema evolution, performance optimization, and Glue Workflows.

4. Does AWS Glue require coding?

AWS Glue Studio offers low-code capabilities, but knowing Python, PySpark, and Spark SQL is very useful.

5. Which job roles commonly require AWS Glue skills?

AWS Glue skills are often needed for roles like Data Engineer, Cloud Engineer, ETL Developer, Analytics Engineer, and Big Data Engineer.