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.
Kim Schmidt is an AWS Partner & Vendor. She's worked for or with Dun & Bradstreet, Google, Microsoft, & AWS.
Kim is currently writing a book "Artificial Intelligence & Analytics on AWS."
Section Introduction Transcripts
Section Introduction Transcripts
Course Overview Hi everyone. My name is Kim Schmidt, and welcome to my course, Serverless Analytics on AWS. I am an AWS partner, vendor, and consultant via my company, DataLeader.io. DataLeader specializes in big data architecture and advanced analytics on AWS. This course is about best practices when doing big data on AWS, that far exceeds learning how to work with AWS Glue and Amazon Athena. Those two technologies have changed the way big data solutions are architected in the day of AI and ML. Some of the major topics and solutions to business problems that we will cover include how to solve the business problem of heterogenous data transformation, instantaneous querying of heterogenous data for immediate actionable insights without the need to first perform ETL, and among other really, really cool things is having one centralized repository for all your global data, no matter where it physically sits to be in the Glue data catalog, enabling multiple people to do processing and analytics simultaneously, having different permutations of the source data without affecting the actual data. By the end of this course, you will have a thorough understanding of modern distributed advanced analytics workflows that you can begin to immediately apply to your own applications on AWS. Before beginning the course, you should have an AWS account, everything else is taught in the course, and any prerequisites required before starting the course are covered first in the modules. I hope you'll join me on this journey to learn all about AWS Glue and Amazon Athena with my Serverless Analytics on AWS course, at Pluralsight.