Pursuant
How MEV Built a Star Schema Data Warehouse for Reporting Operations
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Star schema model used to structure Pursuant’s reporting data around gift and donation activity.
Client:
Pursuant
Industry:
Fundraising and marketing
Nonprofit donor engagement
Nonprofit donor engagement
Focus:
Star schema data warehouse / Fact and dimension modeling / Reporting-ready data transformation
Technologies:
AWS S3 / AWS Glue Crawler / AWS Glue Catalog / AWS Glue Jobs / Apache Parquet / AWS Redshift Spectrum
[ about the client/ ]
Pursuant is a fundraising and marketing agency that helps nonprofit organizations improve donor engagement through analytics and direct response services. Its account teams relied on internal reports built from client data such as donor demographics, donation amounts, donation frequency, and campaign response rates.
What Needed to Be Done
Pursuant needed to move beyond raw operational data and manual reporting prep. The company’s Microsoft SQL Server data was useful, but it was not structured in a way that made reporting easy, consistent, or scalable across more clients.
The job was to turn operational data into a reporting-ready warehouse model.
The job was to turn operational data into a reporting-ready warehouse model.
Why It Mattered
Reporting systems need more than data storage. They need data shaped around how the business asks questions.
For Pursuant, account managers needed to understand campaign effectiveness, donor behavior, donation activity, and response patterns. That required a data model that could support analytics cleanly, instead of forcing the team to prepare data manually each time.
A star schema gave the reporting layer a clearer structure: facts for measurable activity, dimensions for business context.
For Pursuant, account managers needed to understand campaign effectiveness, donor behavior, donation activity, and response patterns. That required a data model that could support analytics cleanly, instead of forcing the team to prepare data manually each time.
A star schema gave the reporting layer a clearer structure: facts for measurable activity, dimensions for business context.
What MEV Built
MEV built the warehouse MVP using a star schema approach. Operational data was transformed into fact and dimension models, making it easier to support internal analytics and reporting workflows.The warehouse became the structured middle layer between raw SQL Server exports and BI reporting in Tableau.
Technical Flow
01:
Source data exported from Microsoft SQL Server
Operational data was moved from Microsoft SQL Server into AWS S3 using AWS DMS.
02:
Raw snapshots stored in S3
The exported data was stored as compressed Parquet files, keeping the source layer close to the original database structure.
03:
Schemas created with AWS Glue
AWS Glue Crawlers scanned the exported files and created schemas in AWS Glue Catalog.
04:
Data transformed with AWS Glue Jobs
Glue Jobs transformed relational OLTP data into warehouse-ready models.
05:
Star schema model created
The warehouse used fact and dimension tables to make reporting data easier to query, understand, and extend.
06:
Reporting-ready layer prepared for BI
The transformed data was written into a conformed S3 layer and exposed to Tableau through Redshift Spectrum.
Outcome
Pursuant gained a cleaner reporting foundation: data was no longer just exported from the source system; it was modeled for reporting. The star schema made the warehouse easier to use for analytics, easier to extend for additional clients, and better suited for future reporting needs.
This case is part of the full Pursuant case study:
MEV Built an AWS Data Warehouse and Automated Reporting Pipeline for Pursuant[ portfolio/ ]
