A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source…

An AWS Glue job typically processes a single, predefined task. For an ETL pipeline with 10 source systems, different file types, and schema changes, a single job is insufficient; a workflow is needed to orchestrate multiple jobs and crawlers.

An Amazon EventBridge rule scheduled every 15 minutes can reliably trigger an AWS Glue workflow. An AWS Glue workflow can orchestrate multiple AWS Glue jobs and crawlers, allowing it to process different file types (.csv, JSON, Parquet), handle large file sizes, and manage schema evolution with crawlers, then load data into Redshift in a structured and resilient manner.

Invoking an AWS Glue crawler when a file is loaded only updates the Data Catalog schema; it does not perform the extract, transform, and load operations required for the full ETL pipeline.

Configuring an AWS Lambda function to invoke an AWS Glue workflow when a new file is loaded into S3 provides an event-driven mechanism. This allows the ETL pipeline to react immediately to new data. Similar to the EventBridge trigger, the AWS Glue workflow provides the necessary orchestration for processing diverse file types, handling schema changes via crawlers, and loading data into Redshift for varying file sizes.

Invoking an individual AWS Glue job for each file upload is less efficient for managing a complex ETL process involving multiple source systems, schema evolution, and diverse file types compared to orchestrating these tasks within an AWS Glue workflow.