Best practices for bulk data loading in AWS Redshift - Glue or Copy

Question

What are the pros and cons when it comes to using **AWS Glue** over Redshift's internal functions (such as **COPY** and **INSERT**)? for bulk data loading (In terms of **cost, time, and adaptability**). It's really appreciated if you can provide some examples use cases.

Accepted Answer

Hi,
AWS Glue is an ETL service: T is the key letter. If you need to transform the source data before your load into RedShift, Glue will be highly useful.

For example, Glue provides lots of wired in simple and adanced transformations that you can integrate in your Glue-Based ETL pipeline: see https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-transforms.html

Also, you may want to measure the quality of your data, before loading it to ensure constant quality.  Then AWS Glue Data Quality may be very helpful: see https://aws.amazon.com/blogs/big-data/getting-started-with-aws-glue-data-quality-from-the-aws-glue-data-catalog/

Hope it helps,

Didier

Best practices for bulk data loading in AWS Redshift - Glue or Copy

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