The 2020 Data Pipelines Market Study has revealed that more than 65% of companies prefer cloud or hybrid cloud data integration solutions, and it’s further booming in 2024. One such trending solution is AWS Glue. It is a serverless and fully managed ETL (Extract, Transform, and Load) solution.Thus, learning AWS Glue to simplify your complex ETL workflows is necessary. It helps optimize your data pipelines while leveraging robust cloud-based solutions. You should have a profound knowledge of this tool if you are also preparing for cloud services and data processing job interviews. Basic AWS Glue Interview QuestionsWhat is AWS Glue?AWS Glue can be used to schedule and manage jobs in AWS. We can set up different triggers, like daily events, to automate workflows and boost productivity. It also handles ETL tasks and makes our data ready for automatic analysis. Describe AWS Glue ArchitectureAWS Glue helps users create a Data Catalog and process ETL dataflows. This is how its architecture works:Users create tasks to handle extracting, transforming, and loading (ETL) data from a source to a target.A crawler scans data sources and populates the Data Catalog with table definitions. Users can also manually define tables for streaming sources.The Data Catalog contains metadata needed for ETL operations.AWS Glue can generate a transformation script, or users can provide one via the console or API.Tasks can start immediately or be triggered by a timer or event.When a task starts, the script pulls, transforms, and sends data to the target using an Apache Spark environment in AWS Glue.How would you explain the difference between AWS Glue ETL and AWS Data Pipeline?AWS Glue ETL is all about the preparation of data and its transformation for analytics. AWS Data Pipeline helps you focus on data insights by simplifying pipeline setup and reducing the time spent on development and maintenance of daily data tasks.Yet, AWS Data Pipeline allows for custom workflow definitions and task scheduling across a wide range of services and environments, including on-premises resources. In terms of data sources, AWS Glue supports various options like S3, RDS, and Redshift, whereas Data Pipeline is compatible with both AWS services and external data sources. Workflow definition in Glue includes automatic schema inference and predefined transformations, while Data Pipeline offers customizable workflows with task dependencies and scheduling.What is the use of a Glue Classifier?A Glue Classifier crawls a data store to create metadata tables in the AWS Glue Data Catalog. You can set up a list of classifiers for your crawler. If the first classifier doesn't recognize the data, the crawler moves to the next one until it finds a match.What are some of the key terms used in AWS Glue?Its key terminologies are data catalog, classifier, connection, crawler, database, data store, data source, data target, development endpoint, job, notebook server, script, and table.Intermediate AWS Glue Interview QuestionsHow are AWS Glue and AWS Lake Formation related?AWS Lake Formation leverages the shared infrastructure of AWS Glue. It offers handy features like console controls, ETL code development, task monitoring, a shared data catalog, and a serverless setup. How to add metadata to the AWS Glue Data Catalog?You can add metadata to the AWS Glue Data Catalog in a few ways. Glue crawlers automatically analyze your data stores, extracting schemas and partition structures to populate the catalog with table definitions and statistics. You can also manually add or modify table details using the AWS Glue Console or API. Additionally, on an Amazon EMR cluster, you can run Hive DDL statements through the Amazon Athena Console or a Hive client.What are Bookmarks in AWS Glue?This feature in AWS Glue helps ETL jobs keep track of processed data. It also prevents work duplication as it only handles the new data added since the last job run. It is further helpful in fast processing and reduced costs of ETL workflows where new data is constantly added to the data sources. Advanced AWS Glue Interview QuestionsWhat is your definition of Schema Evolution Management in AWS Glue? How would you handle it?I handle schema evolution in AWS Glue by ensuring ETL jobs adjust to changes in the data schema over time. When new columns are added, existing ones are changed, or some are removed, I manage these changes using AWS Glue’s approaches to keep the ETL process smooth.So, I’ll do the following to handle schema evolution:glueContext = GlueContext(SparkContext.getOrCreate()) dynamic_frame = glueContext.create_dynamic_frame.from_catalog( database="my-database", table_name="my-table", transformation_ctx="datasource0", additional_options={"jobBookmarkKeys":["id"], "jobBookmarkKeysSortOrder":"asc"} ) dynamic_frame = ApplyMapping.apply(frame = dynamic_frame, mappings = [("col1", "string", "col1", "string"), ...], transformation_ctx = "applymapping1")How would you determine the version of Apache Spark used in AWS Glue?That’s pretty simple! For this purpose, we are required to check the Glue version number displayed on the AWS Glue console. You can also retrieve it using the command: aws glue get-spark-version.How can you add a trigger in AWS Glue using the AWS CLI?You can use the following command to add a trigger:aws glue create-trigger --name MyTrigger --type SCHEDULED --schedule "cron(0 12 * * ? *)" --actions CrawlerName=MyCrawler --start-on-creationI’ll use this command to create a scheduled trigger called MyTrigger. Next, it will launch a crawler named MyCrawler daily at 12:00 UTC.Real-world Scenario-based Interview QuestionsHow do you process incremental updates in a data lake with AWS Glue?You can use a Glue Crawler to detect changes in the source data and update the Glue Data Catalog. Then, create a Glue job to extract, transform, and append the updated data to the data lake. AWS Glue's incremental loading feature can also be used to load the data.What steps do you follow to transform a JSON file in S3 using Glue and load it into AWS Redshift?First, I will use a Glue Crawler to determine the schema of the JSON file in S3, which will help me create a Glue Data Catalog table.Next, I will create a Glue job to extract the JSON data from S3 and apply transformations using either built-in Glue transformations or custom PySpark or Scala code.Finally, I will load the transformed data into the Redshift table using the Redshift connector.Final ThoughtsSo, these are some of the key concepts you should prepare beforehand while going for an AWS Glue interview. Ensure it’s not all you do! Try to find similar topics related to the subject and delve deep into each to reserve your spot as the best candidate ever.Read Morehttps://devopsden.io/article/aws-data-engineer-interview-questionsFollow us onhttps://www.linkedin.com/company/devopsden/
