![]() After the ETL job has been corrected, you may want to implement monitoring and set standard alert thresholds for your AWS Glue environment. The next steps usually include completing an analysis of the jobs, looking at cost reports to see which account generated the spike in usage, going through logs to see when what happened with the job, and so on. During testing, one of the jobs wasn’t configured to automatically scale its compute resources, resulting in jobs timing out, costing the organization more than anticipated. This team is allowed to create AWS Glue for Spark jobs in development, test, and production environments. Picture a scenario where you, the VP of Data and Analytics, are in charge of your data and analytics environments and workloads running on AWS where you manage a team of data engineers and analysts. Many organizations today are using AWS Glue to build ETL pipelines that bring data from disparate sources and store the data in repositories like a data lake, database, or data warehouse for further consumption. These organizations are looking for ways they can reduce cost across their IT environments and still be operationally performant and efficient. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset. Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large.
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