Create Redshift Analytics Marts with dbt

Model Gold lakehouse data into reporting-ready Redshift analytics marts using dbt, external schemas, serving models, and audit checks.
AWS • Analytics • Advanced • Healthcare

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What this project solves

Gold lakehouse tables are clean and business-ready, but teams usually need focused reporting datasets for specific use cases.

Operations teams may need patient flow and service metrics, clinical quality teams may need outcome-focused datasets, finance teams may need revenue views, and leadership teams may need executive scorecards.

In this pipeline, you will connect Amazon Redshift to Gold lakehouse tables on Amazon S3 and use dbt to create analytics-ready marts. You will build staging models, serving models, business marts, audit checks, and freshness monitoring so the data can be used for dashboards, analytics, and decision-making.

What you’ll build

  • Connect Amazon Redshift to Gold lakehouse tables
  • Create external schemas backed by AWS Glue Catalog
  • Set up dbt for analytics engineering
  • Build staging and serving layer models
  • Create operations, clinical quality, finance, and executive marts
  • Add audit checks and freshness monitoring models
  • Prepare reporting-ready datasets for BI tools

How you’ll build it

  • Start from Gold Iceberg tables on Amazon S3
  • Create Redshift external schemas for lakehouse access
  • Use dbt to build staging and serving models
  • Create business marts for clinical, operations, finance, and leadership use cases
  • Validate row counts, freshness, and audit checks before publishing

Tools you’ll use

Amazon Redshift Serverless • dbt • Amazon S3 • Apache Iceberg • AWS Glue Catalog • SQL • Redshift External Schemas

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Explain how lakehouse data is served to analytics teams
  2. Connect Redshift to Gold lakehouse tables on Amazon S3
  3. Build analytics transformations using dbt
  4. Create reporting-ready marts for business teams
  5. Monitor freshness, row counts, and audit checks in analytics models

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