20 Latest AWS Glue Interview Questions and Answers for 2024

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20 Latest AWS Glue Interview Questions and Answers for 2024
 |  BY Daivi

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. It is a popular ETL tool well-suited for big data environments and extensively used by data engineers today to build and maintain data pipelines with minimal effort. Its integration with other popular AWS services like Redshift, S3, and Amazon Athena makes it a valuable tool for data engineers to build end-to-end data engineering projects. If you are preparing for your ETL developer or data engineer interview, you must possess a solid fundamental knowledge of AWS Glue, as you’re likely to get asked questions that test your ability to handle complex big data ETL tasks. This blog will discuss some popular AWS Glue interview questions and answers to help you strengthen your AWS Glue knowledge and ace your big data engineer interview.


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20 AWS Glue Interview Questions and Answers 

Here are 15 AWS Glue interview questions and answers to test your knowledge of the ETL tool and help you land your dream ETL job.

AWS Glue Job Interview Questions For Experienced

  1. Mention some of the significant features of AWS Glue.

You can leverage AWS Glue to discover, transform, and prepare your data for analytics. In addition to databases running on AWS, Glue can automatically find structured and semi-structured data kept in your data lake on Amazon S3, data warehouse on Amazon Redshift, and other storage locations. Glue automatically creates Scala or Python code for your ETL tasks, which you can modify using tools you are already comfortable with. Furthermore, AWS Glue DataBrew allows you to visually clean and normalize data without any code.

  1. What is the process for adding metadata to the AWS Glue Data Catalog?

There are several ways to add metadata to the AWS Glue Data Catalog using AWS Glue. The Glue Data Catalog is loaded with relevant table definitions and statistics as the Glue crawlers automatically analyze different data stores you own to deduce schemas and partition structures. Alternatively, you can manually add and change table details using the AWS Glue Console or the API. On an Amazon EMR cluster, you can also execute Hive DDL statements via the Amazon Athena Console or a Hive client.

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  1. What client languages, data formats, and integrations does AWS Glue Schema Registry support?

The Schema Registry supports Java client apps and the Apache Avro and JSON Schema data formats. The Schema Registry is compatible with apps made for Apache Kafka, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda.

  1. Does the AWS Glue Schema Registry offer encryption in both transit and storage?

Yes, with API calls that use TLS encryption over HTTPS, your customers' communication with the Schema Registry is encrypted as it transits between them. A service-managed KMS key is always used to encrypt schemas while they are being stored in the Schema Registry.

  1. Where do you find the AWS Glue Data Quality scores?

Scores for data quality are displayed in your table's Data Quality page from the Data Catalog. When creating an AWS Glue Studio job, you can see your data pipeline scores by selecting Data Quality. Your data quality jobs can be set up to publish their results to an Amazon Simple Storage Service (S3) bucket. Then, you can use Amazon QuickSight or Amazon Athena to query this data.

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AWS Glue Technical Interview Questions

  1. In the AWS Glue Catalog, how do you list databases and tables?

Using the command below, one can list databases and tables:

import boto3

client = boto3.client('glue',region_name='us-west-1')

responseGetDatabases = client.

get_databases()

databaseList = response

GetDatabases['DLIST']

for databaseDict in databaseList:

   databaseName = databaseDict['ABC']

    print '\ndatabaseABC: ' + databaseXYZ

response

GetTables = client.

get_tables( DatabaseName = databaseXYZ )

tableList = response

GetTables['TLIST']

for tableDict in tableList:

     tableName = tableDict['DEF']

     print '\n-- tableDEF: '+tableDEF

  1.  How will you modify Duplicating Data using AWS Glue?

Using the following command, AWS Glue enables you to update Duplicating Data:

sc = SparkContext()

glueContext = GlueContext(sc)

src_data = create_dynamic_frame.from_catalog(database = src_fg, table_name = src_fg)

src_df =  src_data.toDF()

dst_data = create_dynamic_frame.from_catalog(database = dst_fg, table_name = dst_fg)

dst_df =  dst_data.toDF()

merged_df = dst_df.union(src_df)

merged_df.write.format('xyz').

  1. In AWS Glue, how do you enable and disable a trigger?

A trigger can be turned on or off using the AWS Glue console, AWS Command Line Interface (AWS CLI), or AWS Glue API. For example, you can use the following commands to start or stop triggers using the AWS CLI-

aws glue start-trigger --name MyTrigger  

aws glue stop-trigger --name MyTrigger

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  1. How do you identify which version of Apache Spark is AWS Glue using?

By looking at the Glue version number, you can find out which version of Apache Spark is being used by AWS Glue. The version number is shown on the AWS Glue console, and you can also retrieve it using the following command: aws glue get-spark-version.

  1. How do you add a trigger using the AWS CLI in AWS Glue?

You must enter a command similar to the one below.

aws glue create-trigger --name MyTrigger --type SCHEDULED --schedule  "cron(0 12 * * ? *)" --actions CrawlerName=MyCrawler --start-on-creation  

With this command, a crawler named MyCrawler is launched along with a schedule trigger called MyTrigger that runs daily at 12:00 UTC.

AWS Glue Scenario-based Interview Questions

  1. Suppose there is a communication issue with OnPrem, and it is necessary for the job to be automatically re-executed to ensure data integrity. Can you find a way for a job to retry execution after a failure?

The MaxRetries option in Glue has a native retry mechanism. If using Glue Studio, the "Job Details" tab allows you to define this parameter programmatically.

MaxRetries – Number (integer). The maximum number of attempts to retry this job once a JobRun fails.

  1. How do you handle incremental updates to data in a data lake using Glue?

You can mention using Glue Crawler to identify any changes in the source data and update the Glue Data Catalog accordingly. After which, you can create a glue job that uses the Glue Data Catalog table to extract the updated data from the source, transforms it, and appends it to the data in the data lake. Also, you can use AWS Glue’s incremental loading feature to load the data.

  1. Suppose that you have a JSON file in S3. How will you use Glue to transform it and load the data into an AWS Redshift table?

  • Use glue crawler to find out the schema of the JSON file in S3. This will help create a glue data catalog table.

  • Create a glue job to extract the JSON data from S3 and apply transformations either by using in-built glue transformations or by writing custom PySpark or Scala code.

  • Transformed data can then be loaded into the Redshift table using the redshift connector.

 

  1. How would you extract data from the ProjectPro website, transform it, and load it into an Amazon DynamoDB table?

  • Create a glue job to use the in-built glue web scraping library to scrape and extract data from ProjectPro website.

  • Transform the extracted data into a format that can be loaded into DynamoDB table using the Dataframe API.

  • Use DynamoDB glue connector to load the data.

 

  1. Assume you’re working for a company in the BFSI domain with lots of sensitive data. How can you secure this sensitive information in a glue job?

You can answer this question by mentioning using AWS Key Management Service, which lets you encrypt sensitive data. Another probable solution is using in-built support for data redaction and masking provided by Glue to redact or mask the sensitive data.

AWS Glue Real-Time Interview Questions (Open-Ended)

  1. Explain a project you’ve worked on wherein you created an ETL job using AWS glue.

  2. What steps do you follow to monitor the cost and performance of a Glue job?

  3. Have you integrated any other AWS big data services with Glue? If yes, which ones and how?

  4. Have you ever come across errors when creating a glue job? If yes, how do you handle or troubleshoot errors?

  5. What measures have you implemented to optimize the performance of your glue job when working with big data?

 

You should also have practical experience with real-world AWS projects that showcase your skills and expertise if you want to surpass your competitors. Explore the ProjectPro repository to access industry-level big data and data science projects. 

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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