5 Must-know AWS Glue Interview Questions Beforehand

Here are the top AWS Glue interview questions on jobs and monitoring. These can be expected in any interview and are helpful to review beforehand.

AWS Glue Interview Questions

01. What is an AWS Glue job?

An AWS Glue job is a service from Amazon Web Services that helps you create and run scripts to transform and move data between different sources and targets. It’s a serverless ETL (Extract, Transform, Load) service.

2. Can you explain the difference between Spark Vs. AWS Glue job?

In AWS Glue, a job is essentially an Apache Spark application that performs the ETL operations. However, AWS Glue abstracts away many of the complexities of managing Spark infrastructure, such as cluster provisioning and scaling. AWS Glue provides features like automatic schema inference, job monitoring, and integration with other AWS services. Here’s more on the Spark job.

03. How do you schedule AWS Glue jobs?

AWS Glue jobs can be scheduled using triggers. Triggers can be based on a time schedule or event-based (such as when a new file is added to an Amazon S3 bucket). Configure triggers using the AWS Management Console, AWS SDKs, or AWS CLI.

04. What are the different types of AWS Glue job run modes?

• AWS Glue supports three job run modes: “Run Once,” “Start on Demand,” and “Run on Schedule.”
• “Run Once” mode executes the job only once.
• “Start on Demand” mode allows you to manually start the job.
• “Run on Schedule” mode enables you to schedule the job to run at specific intervals.

05. How can you monitor and debug AWS Glue jobs?

• AWS Glue provides monitoring and debugging capabilities through AWS CloudWatch logs and metrics. CloudWatch logs capture logs generated by the job execution, which helps identify root causes.
• Additionally, AWS Glue generates job run metrics, such as execution time and success/failure status, which can be monitored through CloudWatch metrics.
• Moreover, AWS Glue provides a job bookmarking feature, which helps in resuming the job from the point of failure during incremental data processing.

References

• AWS Glue documentation