Introduction
Struggling to predict the exact AWS Glue questions you’ll face in interviews is a common pain — you need focused, practical preparation, not scattershot memorization. This guide, centered on Top 30 Most Common AWS Glue Interview Questions You Should Prepare For, gives concise, job-focused answers and real-world context so you can answer clearly under pressure. Read this to sharpen your technical explanations, practice scenario responses, and show interviewers you understand design choices and trade-offs.
Start here, practice the Q&A, and convert knowledge into confident interview language.
Yes — these are the Top 30 Most Common AWS Glue Interview Questions You Should Prepare For.
Short answer: interviewers expect clear definitions, architecture knowledge, real-world scenarios, and cost/performance trade-offs.
AWS Glue sits at the intersection of ETL, metadata, and the AWS data ecosystem; interviewers test whether you can design scalable Glue jobs, manage the Data Catalog, integrate with services like S3 and Redshift, and control costs with DPUs and monitoring. Expect a mix of conceptual, scenario-based, and behavioral prompts. Takeaway: practice concise definitions and one- or two-sentence trade-offs you can deliver under pressure.
Technical Fundamentals
Q: What is AWS Glue?
A: A managed ETL service that discovers, catalogs, and transforms data for analytics and data lakes.
Q: What is the AWS Glue Data Catalog?
A: The centralized metadata repository that stores table definitions, schemas, and partitions used by Glue and other AWS services.
Q: How does an AWS Glue ETL job work?
A: A job runs code (Scala/Python) on DPUs to extract, transform, and load data, using Data Catalog metadata and connectors.
Q: What is an AWS Glue Crawler and how is it used?
A: A crawler inspects data stores, infers schemas, and populates the Data Catalog with table and partition metadata.
Q: What is a Data Processing Unit (DPU) in AWS Glue?
A: A DPU is a unit of processing capacity (CPU, memory) used to scale Glue jobs; you choose DPUs to balance performance and cost.
Q: What are Glue development endpoints and when do you use them?
A: Development endpoints provide an environment to develop and debug ETL scripts interactively against the Data Catalog and your data.
Scenario-Based and Project Experience
Q: How would you design a Glue job to ingest semi-structured JSON from S3 into Parquet?
A: Use a crawler for schema inference, write a PySpark Glue job to parse JSON, normalize nested fields, and write Parquet partitions for query efficiency.
Q: Describe a Glue workflow for incremental loads.
A: Use crawlers or schema registry for metadata, Glue jobs to read change files, apply dedupe logic, and triggers to orchestrate incremental pipeline runs.
Q: How do you implement error handling and retries in Glue jobs?
A: Use try/except in scripts, configure job bookmarks for idempotence, set job retries, and send CloudWatch alarms for failing runs.
Q: How would you handle evolving schemas in Glue?
A: Use schema registry or crawler with versioning, design ETL to handle nullable fields, and use transformation logic to migrate older records.
Q: Explain using Glue triggers in a production pipeline.
A: Triggers start jobs on schedules, on job completion, or via events; combine scheduled and conditional triggers for complex workflows.
Q: How do you demonstrate Glue experience on a resume for interviews?
A: Note measurable impacts: datasets processed, cost reductions, DPU choices, latency improvements, and specific integrations (S3, Redshift).
(For extended scenarios and walkthroughs, see the AWS Glue scenario examples referenced on YouTube and Dev.to.) YouTube Dev.to
Takeaway: convert project outcomes into concise STAR stories that highlight design decisions and trade-offs.
Cost, Performance Monitoring, and Optimization
Q: How do you monitor AWS Glue job performance and cost?
A: Use CloudWatch metrics, job logs, and AWS Cost Explorer to track DPU usage, runtime, and cost per job.
Q: What are best practices for setting DPU capacity?
A: Start with moderate DPUs, profile runtime, observe memory/CPU bottlenecks, then scale DPUs to hit SLAs while controlling cost.
Q: How can you optimize Glue job startup and shuffle times?
A: Optimize partitioning, reduce shuffles through data co-location, use pushdown predicates, and cache small dimension tables.
Q: When should you use job bookmarks?
A: Use bookmarks for incremental processing to avoid reprocessing data and to reduce runtime and cost.
Q: How do you profile Glue jobs to find bottlenecks?
A: Analyze CloudWatch logs, Spark UI (if available through dev endpoints), and instrument code to measure stage durations and shuffle sizes.
Q: What cost-conscious alternatives exist for small jobs?
A: Use AWS Glue Elastic Views or serverless alternatives, smaller DPUs, or AWS Lambda for light transforms to reduce overhead.
(For monitoring examples, ProjectPro and Verve Copilot provide practical tips.) ProjectPro Verve Copilot Blog
Takeaway: explain how you measured and reduced cost with concrete metrics during interviews.
AWS Glue Integration and Ecosystem
Q: How does AWS Glue integrate with Amazon S3?
A: Glue reads and writes data stored in S3; crawlers inspect S3 objects to populate the Data Catalog for downstream queries.
Q: How is the Glue Data Catalog used with Redshift and Athena?
A: Athena and Redshift Spectrum query tables defined in the Data Catalog, enabling serverless interactive queries over S3 data.
Q: What is Glue schema registry and when to use it?
A: A registry for versioned schemas used by streaming ETL to enforce contract compatibility across producers and consumers.
Q: How does Glue work with Lake Formation for governance?
A: Lake Formation centralizes permissions and access controls for catalog tables; integrate Glue to enforce fine-grained data access.
Q: What is AWS Glue Elastic Views?
A: Elastic Views materializes and synchronizes data across stores for simplified, near-real-time replication and view maintenance.
Q: When would you choose Glue over EMR or custom Spark?
A: Choose Glue for managed ETL with integrated catalog and lower operational overhead; use EMR when you need custom Spark tuning and specialized libraries.
(MindMajix and ProjectPro cover Glue’s ecosystem in depth.) MindMajix ProjectPro
Takeaway: show awareness of when to use Glue vs other AWS services and how governance fits into design.
Interview Preparation Strategies and Behavioral Questions
Q: How should you structure answers to AWS Glue scenario questions?
A: Use a problem–approach–result format: state the requirement, explain architecture/design choices, and close with measurable results.
Q: What behavioral questions are common for AWS Glue roles?
A: Expect leadership and teamwork prompts: describe a time you reduced ETL cost, fixed a production failure, or persuaded stakeholders on a design.
Q: How do you prepare STAR stories about Glue projects?
A: Quantify scope (data volume, frequency), highlight your role in design and implementation, and state the impact (cost saved, latency improved).
Q: What common mistakes should you avoid in Glue interviews?
A: Avoid vague answers, ignore trade-offs, or failing to tie technical choices to business outcomes.
Q: How do you highlight Glue skills during live coding or whiteboard sessions?
A: Communicate trade-offs (cost vs latency), sketch architecture, and describe failure modes and monitoring.
Q: How can you rehearse answers effectively?
A: Practice concise responses, simulate interviewer follow-ups, and use logs/metrics from past projects as evidence.
(Hirist and igotanoffer provide frameworks for behavioral prep relevant to AWS teams.) Hirist I Got an Offer
Takeaway: pair technical depth with behavioral clarity to stand out in interviews.
How Verve AI Interview Copilot Can Help You With This
Verve AI Interview Copilot accelerates your prep by simulating Glue interview scenarios, giving structured feedback on clarity, architecture rationale, and cost/performance trade-offs. It helps convert technical knowledge into crisp STAR narratives, suggests concise code snippets or pseudo-code for Glue jobs, and adapts prompts to gaps it detects in your answers. Use Verve AI Interview Copilot during mock interviews, review suggested improvements, and rehearse follow-up answers until they feel natural. Try specific Glue scenario prompts and get instant, targeted coaching from Verve AI Interview Copilot to reduce stress and increase precision. For hands-on script clarity and live reasoning, open Verve AI Interview Copilot in your prep sessions.
What Are the Most Common Questions About This Topic
Q: Can Verve AI help with behavioral interviews?
A: Yes. It applies STAR and CAR frameworks to guide real-time answers.
Q: What is the Glue Data Catalog?
A: Central metadata store used by Glue, Athena, and Redshift Spectrum.
Q: How are DPUs related to cost?
A: DPUs determine compute consumed; more DPUs increase cost but reduce runtime.
Q: When use a crawler vs manual schema?
A: Use crawlers for ad hoc sources; manual schema for stable, production pipelines.
Q: How do I show impact in Glue projects?
A: Use metrics: data volume, runtime, cost saved, and query performance improvements.
Conclusion
Focused practice on the Top 30 Most Common AWS Glue Interview Questions You Should Prepare For sharpens your explanations, helps you choose clear trade-offs, and turns project experience into persuasive interview stories. Structure answers, quantify impact, and rehearse scenario responses to build confidence and clarity. Try Verve AI Interview Copilot to feel confident and prepared for every interview.
