In this series, I've introduced AWS Glue and compared it to traditional ETL tasks performed in a role such as a business intelligence engineer. I also detailed the most important parts of Glue to extract, transform and load data from point A, our source data, to point B, a destination file or data repository. AWS Glue performs a lot of this work for you. Remember that the reward is prepared, consumable data from better analysis. The transformation is often the more heavy lifting work to combine from a variety of sources, clean the data, map correctly to the destination, form into a new schema, aggregate or disaggregate and more. 

I detailed the benefits of using AWS Glue and these include ETL code in AWS Glue easily runs serverless. AWS Glue has a crawler that infers schemas for source, working and destination data and the crawler can run on a schedule to detect changes and AWS Glue auto-generates ETL scripts as a starting point for customizing in either Python or Scala. 

I also provided a demonstration to show you how to use the features of AWS Glue to create a crawler to generate metadata for the source file which facilitates our mapping from source to destination. I showed you how to create a simple job and after running, I showed you the transformed data inside a table in a MySQL database. Lastly, you should consider further exploration of AWS Glue and here are some good steps you can take to expand your ability in AWS Glue. You can learn Python or Scala and now that you know how to set up and create an AWS Glue job, I recommend that you learn Python code so you can customize your ETL job scripts to give you more flexibility. 

A repository in GitHub called AWS Glue ETL Code Samples, as shown with the link on screen, offers examples to help you with creating relationships between tables in your destination after loading, undo and redo results from your call operation and more. There are also some examples for Scala as well. You should practice with data that interests you and you can find more examples of programmatic data manipulation. I recommend visiting kaggle.com and downloading a variety of data files and uploading to AWS S3 and then combining and drawing correlations. Become familiar with AWS QuickSight and other visualization tools and remember that the analysis of data is the end goal after the work of transforming and loading is completed and likely automated. 

Power BI Desktop is a powerful visualization tool that can be downloaded for free and here is an AWS Big Data blog on 10 visualizations to try in Amazon QuickSight with sample data. 

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