20 AWS Glue Interview Questions and Answers

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. AWS Glue automatically discovers and profiles data via the AWS Glue Data Catalog, recommends and generates ETL code to transform your data, and runs the ETL jobs on a fully managed, scale-out Apache Spark environment. In this article, we will discuss some common AWS Glue interview questions.

AWS Glue Interview Questions and Answers

Here are 20 commonly asked AWS Glue interview questions and answers to prepare you for your interview:

1. What is AWS Glue?

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it easy for customers to prepare and load their data for analytics. AWS Glue can read, write, and process data stored in Amazon S3, Amazon DynamoDB, Amazon RDS, Amazon Redshift, and any other data store that is accessible through a JDBC driver.

2. Can you explain what a crawler is in the context of AWS Glue?

A crawler is a program that scans data sources and extracts metadata from them. This metadata is then used to populate the AWS Glue Data Catalog with table definitions, column information, and other relevant details. Crawlers can also be used to keep the Data Catalog up-to-date as your data sources change over time.

3. What are some advantages of using AWS Glue?

AWS Glue has a number of advantages over traditional ETL tools, including the following:

– It is serverless, so there is no need to provision or manage any infrastructure.
– It is highly scalable, so it can handle very large data sets.
– It is flexible, so it can be used for a variety of data transformation tasks.
– It integrates with a number of other AWS services, making it easy to set up a complete ETL pipeline.

4. Why would you use AWS Glue over traditional ETL tools like Informatica or DataStage?

AWS Glue is a cloud-based ETL tool that is more cost-effective and easier to use than traditional on-premise ETL tools. It is also more scalable and flexible, making it a good choice for organizations that are looking to move to the cloud or that have large amounts of data to process.

5. How do you use AWS Glue to transform data from one format to another?

You can use AWS Glue to transform data from one format to another by using the ETL (extract, transform, and load) capabilities of the service. With AWS Glue, you can extract data from a variety of sources, transform it into the desired format, and then load it into an AWS data store.

6. What’s the difference between a Classifier and a Crawler in AWS Glue?

A Classifier is used to categorize data stored in S3 so that it can be used by AWS Glue for ETL purposes. A Crawler is used to scan data stored in S3 and populate the AWS Glue Data Catalog with information about the data.

7. Is it possible to schedule an AWS Glue job to run based on events? If yes, then how?

Yes, it is possible to schedule an AWS Glue job to run based on events. You can do this by setting up an event trigger for your job in the AWS Glue console.

8. What are some best practices for working with AWS Glue jobs?

Some best practices for working with AWS Glue jobs include creating jobs that are idempotent, using job bookmarks to keep track of job progress, and using job parameters to make jobs more flexible. Additionally, it is important to test jobs before running them in production, and to monitor job runs to ensure that they are completing successfully.

9. When should I consider using AWS Glue instead of Amazon EMR?

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You should consider using AWS Glue if you want a managed ETL solution that is cost-effective and easy to use. Amazon EMR is a good choice if you need a more flexible and customizable solution, or if you need to process data in real-time.

10. What key features does AWS Glue provide that make it preferable over other cloud-based big data solutions like Google Cloud Platform or Microsoft Azure?

AWS Glue provides a number of key features that make it preferable over other cloud-based big data solutions. First, it is a fully managed service, meaning that you don’t need to worry about provisioning or managing any infrastructure. Second, it offers a data catalog that makes it easy to discover, organize, and query your data. Finally, it has built-in support for a number of data formats, making it easy to get started with data transformation and analysis.

11. What happens if there is an error while running AWS Glue?

If there is an error while running AWS Glue, the job will fail. The job will be retried according to the job’s retry policy.

12. What are some common examples of when you might want to create a custom classifier?

Some common reasons for wanting to create a custom classifier would be if you are working with a new file format that is not yet supported by AWS Glue, or if you want to fine-tune the classification of your data to better suit your needs.

13. Can you give me an example of where you have used AWS Glue to automate data processing?

I have used AWS Glue to automate data processing in a few different ways. One example is creating an automated workflow that ingests data from a variety of sources, transforms it into a consistent format, and then loads it into an Amazon Redshift data warehouse for analysis. Another example is setting up a Glue job to run daily that crawls an S3 data lake, identifies new and changed data, and then updates corresponding tables in a Glue Data Catalog.

14. What is the maximum number of connections allowed by default in AWS Glue?

The maximum number of connections allowed by default in AWS Glue is 10.

15. What is the max size of metadata databases supported by AWS Glue?

The max size of metadata databases supported by AWS Glue is 10 GB.

16. How can you determine which version of Apache Spark is being used by AWS Glue?

You can determine which version of Apache Spark is being used by AWS Glue by checking the Glue version number. The version number is displayed in the AWS Glue console, and you can also find it by running the following command:

aws glue get-spark-version

17. What are Dynamic Frames in AWS Glue?

Dynamic Frames are a way of representing data that is stored in an AWS Glue Data Catalog. Dynamic Frames allow for schema changes to be made on the fly, which is helpful when working with data that is constantly changing or evolving.

18. Can you describe the process used by AWS Glue to generate code for your ETL jobs?

AWS Glue uses a process called “script generation” to automatically generate code for your ETL jobs. This process starts by taking your input data and mapping it to the appropriate AWS Glue Data Catalog tables. Next, AWS Glue uses a built-in transformer to convert your data into the format required by your ETL job. Finally, AWS Glue generates the code for your ETL job and runs it in the appropriate AWS Glue runtime environment.

19. What are some important limitations of using AWS Glue?

Some important limitations to be aware of when using AWS Glue include the following:

– AWS Glue is not able to crawl all types of data sources, so you may need to use a different tool for some data sources.
– AWS Glue can be slow to crawl large data sets.
– AWS Glue may not be able to crawl some data sources that are behind a firewall.

20. What are some examples of applications that benefit from using AWS Glue?

AWS Glue can be used for a variety of different applications, including data warehousing, data lakes, data pipelines, and ETL jobs.