![]() ![]() Should you stick with AWS Glue for ETL?ĪWS Glue is a managed ETL service that you control from the AWS Management Console. In the AWS world, AWS Glue can handle ETL jobs, or you can consider a third-party service like Stitch. If you want to follow Magnusson's advice, you can turn to a SaaS service to handle ETL tasks. There is nothing more soul sucking than writing, maintaining, modifying, and supporting ETL to produce data that you yourself never get to use or consume."įortunately, there's a smart alternative to writing and maintaining your own ETL code. For the love of everything sacred and holy in the profession, this should not be a dedicated or specialized role. As Jeff Magnusson, vice president of Stitch Fix, says, " Engineers should not write ETL. Given all that, many organizations choose to avoid manually coding data pipelines. Repeat many of these steps as they maintain the code over time.Code in security, logging, and alerting capabilities.Write the logic for the extraction process.Learn how to use the data sources' APIs.However, writing ETL code is not simple - among other things, data engineers need to: You then can task a data engineer on your internal team with manually coding a reusable data pipeline. You also need to select a data warehouse destination that provides an architecture that's appropriate for the types of data analysis you plan to run, fits within your budget, and is compatible with your software ecosystem. Then, unless you plan to replicate all of your data on a regular basis, you need to identify when source data has changed. For instance, you first have to identify all of your data sources. DIY data pipeline - big challenge, bad businessĮTL is part of the process of replicating data from one system to another - a process with many steps. This changes the data pipeline process for cloud data warehouses from ETL to ELT. Amazon Redshift is a data warehouse and S3 can be used as a data lake.Ĭloud-native data warehouses like Redshift can scale elastically to handle just about any processing load, which enables data engineers to run transformations on data after loading. In the AWS environment, data sources include S3, Aurora, Relational Database Service (RDS), DynamoDB, and EC2. How ETL worksĮTL is a three-step process: extract data from databases or other data sources, transform the data in various ways, and load that data into a destination. The best approach takes into consideration your data sources, your data warehouse, and your business requirements. Finding the most suitable ETL process for your business can make the difference between working on your data pipeline or making your data pipeline work for you. The complexity of your data landscape grows with each data source, each set of business requirements, each process change, and each new regulation.
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