Hello and welcome to this course where I shall discuss developing for serverless extract, transform and load operations using AWS Glue. First I will focus on the difference between serverless ETL and traditional ETL and provide some background for why AWS Glue is a great tool for a data engineer's arsenal. I will then show some diagrams for how AWS Glue is used and offer a demo of moving data from fictional source data to a couple of AWS destinations using AWS Glue. Lastly, I will discuss the benefits and tradeoffs for developing serverless ETL and using AWS Glue. 

This course has been written and created by Norm Warren and recorded by myself, Stuart Scott. Now, Norm Warren specializes in data development, data visualizations and data analysis using a variety of ETL tools in cloud and on-premise databases and more recently AWS and Microsoft Azure, Tableau and Microsoft BI. Feel free to connect with Norm with any questions using the details shown on the screen. Alternatively, you can always get in touch with us here at Cloud Academy by sending an email to support@cloudacademy.com. 

This segment for data warehouse engineers that want to learn about serverless ETL and AWS Glue, developers that want to learn more about ETL work using AWS Glue, and developer leads that want to learn about the serverless ETL process, and project managers and owners that want to learn more about data preparation. 

This course is divided into five sections and in these videos I will look at the following points: an overview of traditional ETL in comparison to AWS Glue, an overview of AWS Glue itself, I shall perform a demonstration by creating an ETL solution using AWS Glue, I'll also look at some of the use cases for using AWS Glue, and finally a summary which will highlight some of the key points throughout this course. 

This course will provide you with the following: an understanding of serverless ETL which means Extract, Transform, and Loading of data, knowledge of architecture of a typical ETL project between source data and destination databases, data warehouse or big data destinations, an understanding of prerequisite setup of AWS parts to use AWS Glue for ETL, knowledge for how to use AWS Glue to perform serverless ETL, and how to edit ETL processes created from Glue. 

There's a number of prerequisites to this course and these include an understanding of one or more of the data destinations offered by AWS, an awareness of data warehousing principles, it's helpful if you have an understanding of serverless computing, and if you'd like to know more about serverless then you can see our existing course here What Is Serverless Computing, and finally an understanding of object-oriented programming such as Python. 

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