AWS Glue and Amazon Athena have transformed the way big data workflows are built in the day of AI and ML. Learn how to build for now and the future, how to future-proof your data, and know the significance of what you’ll learn can't be overstated.
How to architect and build big data analytics in the AWS cloud in the day of AI and ML has been transformed by both AWS Glue and Amazon Athena. In this course, Serverless Analytics on AWS, you'll gain the ability to have one centralized data source for all your globally scattered data silos regardless if the data is structured, unstructured, or semi-structured so you can perform multiple types of advanced analytics on the data by multiple people simultaneously without affecting the underlying data store wherever in the world each data set is located, keeping the data in sync with any changes to the source data. First, you'll learn how to use AWS Glue Crawlers, AWS Glue Data Catalog, and AWS Glue Jobs to dramatically reduce data preparation time, doing ETL “on the fly”. Next, you’ll discover how to immediately analyze your data without regard to data format, giving actionable insights within seconds. Finally, you’ll explore how to use AWS best practices to keep up by having AI and ML analytics incorporated into your analytics workflows, future-proofing your data via immutable logs. When you’re finished with this course, you'll have the skills and knowledge of using state of the art serverless technologies to provide a myriad of insight types whenever you need them.
Download and Install Course Prerequisites
The State of Analytics in the AWS Cloud
Infrastructure and Data Setup via Amazon CloudFormation
The Power of AWS Glue
Creating AWS Glue Resources and Populating the AWS Glue Data Catalog