Best Practices For AWS Glue ETL
Following are the some of the best practices that you can follow while implementing the AWS Glue ETL.
- Data Catalog: Use data catalog as an centralized metadata repository try to store all the metadata about the data sources, transformations, and targets.
- Crawlers: You need to keep you metadata uptodate for that you can use the crawler to to run the periodically which keeps the metadata up to date.
- Leverage Dynamic Allocators: Dynamic allocates are used to scale up and scale down the workers and executors based up on the load which will store lots of resources.
- Utilize Bulk Loading: Try to use the bulk loading teefforts of chnique which is more efficient educing the number of individual file writes and improving overall performance.
- Monitor and Analyze Job Metrics: WIth the help of cloudwatch you can monitor the performance of the Glue. You can monitor the job metrics such as execution time, resource utilization, and errors, to identify performance bottlenecks and potential issues.
Introduction To AWS Glue ETL
The Extract, Transform, Load(ETL) process has been designed specifically for the purpose of transferring data from its source database to the data warehouse. However, the challenges and complexities of ETL can make it hard to implement them successfully for all our enterprise data. For this reason, Amazon has introduced AWS Glue.
AWS Glue is a fully managed ETL(Extract, Transform, and Load) service that makes it simple and cost-effective to categorize our data, clean it, enrich it, and move it reliably between various data stores. It consists of a central metadata repository known as the AWS Glue data catalog an ETL engine that automatically generates Python code and a flexible scheduler that handles dependency resolution job monitoring. AWS Glue is serverless which means that there is no infrastructure to set or manage a setup.