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In this aricle I cover creating rudimentary Data Lake on AWS S3 filled with historical Weather Data consumed from a REST API. The S3 Data Lake is populated using traditional serverless technologies like AWS Lambda, DynamoDB, and EventBridge rules along with several modern AWS Glue features such as Crawlers, ETL PySpark Jobs, and Triggers.
In this article I give a practical introductory tutorial to using Amazon Redshift as an OLAP Data Warehouse solution for the popular Pagila Movie Rental dataset. I start with a basic overview of the unique architecture Redshift uses to accomplish its scalable and robust use case as an enterprise cloud data warehouse. Then armed with this basic knowledge of Redshift architecture I move on to give a practical example of designing a schema optimal for Redshift based off the Pagila sample dataset.
In this article I dive into partitions for S3 data stores within the context of the AWS Glue Metadata Catalog covering how they can be recorded using Glue Crawlers as well as the the Glue API with the Boto3 SDK.
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