The post illustrates how to create a modern data platform by leveraging the open-source Data Build Tool (dbt) and various AWS services like Amazon Redshift, AWS Glue, AWS Lambda, and Amazon Managed Workflows for Apache Airflow to build a high-performing, efficient data pipeline for a retail use case.

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<p>This article describes how to create a modern data platform using the Data Build Tool (dbt) and various AWS services. It covers a case study of a retail company that migrated and modernized their legacy data platform using dbt and AWS.</p>
<p>Specifically, the article covers:</p>
<ul>
<li>The use case and requirements for the retail company</li>
<li>An overview of the solution architecture using AWS services like Amazon Redshift, AWS Glue, AWS Lambda, Amazon MWAA, and dbt</li>
<li>Data ingestion approaches (real-time and batch) using services like AWS DMS, AWS Glue, and AWS Lambda</li>
<li>Data warehousing layers in Amazon Redshift (landing, certified dataset, user-friendly data mart)</li>
<li>Creating dbt projects and data pipelines</li>
<li>Orchestrating ETL jobs using Amazon MWAA and Airflow DAGs</li>
<li>Conclusion on the benefits of using dbt and AWS for building modern data platforms</li>
</ul>
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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

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