This course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. In 11 videos, the course explores how data lakes store data using a flat structure, and the data are tagged, making it easy to search and query. You will learn how to build a data lake on the AWS cloud by storing data in S3 (simple storage service) buckets. You will learn to set up your data lake architecture lake using AWS Glue, a fully managed ETL (extract, transform, load) service. You will learn to configure and run Glue crawlers, and you will examine how crawlers merge data stored in an S3 folder path; and to use S3 to generate metadata tables in Glue. Learners will use Athena, Amazon's interactive query service as a simple way to analyze data in S3 using standard SQL. Finally, you will examine how to merge the data crawled by our CSV (comma separated values) crawler into a single table.
Learning Objectives
This course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. In 11 videos, the course explores how data lakes store data using a flat structure, and the data are tagged, making it easy to search and query. You will learn how to build a data lake on the AWS cloud by storing data in S3 (simple storage service) buckets. You will learn to set up your data lake architecture lake using AWS Glue, a fully managed ETL (extract, transform, load) service. You will learn to configure and run Glue crawlers, and you will examine how crawlers merge data stored in an S3 folder path; and to use S3 to generate metadata tables in Glue. Learners will use Athena, Amazon's interactive query service as a simple way to analyze data in S3 using standard SQL. Finally, you will examine how to merge the data crawled by our CSV (comma separated values) crawler into a single table.