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BI Dashboard Migration & Data-Mart Architecture

Securing a university master database while modernising reporting

Re-architected a South-African university’s reporting stack from QlikView on a live Oracle master DB to Power BI fed by purpose-built data marts and an orchestrated ETL pipeline.

SQLMeshDagsterMySQLOraclePower BIPythonSQL
Problem Statement

The BI team queried the Oracle master database directly for QlikView reports a model that was insecure, slow on raw data, prone to integrity issues, and limited by QlikView’s poor integration with other tools.

  • Direct master-DB access for reporting was a security risk.
  • Reporting on raw data made dashboards slow to load.
  • Raw data required complex in-report manipulation, hurting integrity.
  • QlikView limited integration with other tools and systems.
Headline Outcomes
+80%master DB protected

Database security

−70%cleaned data

Dashboard load time

−35%orchestrated ETL

Operational cost

The Solution

Introduced staging and reporting data marts in MySQL, an orchestrated ETL pipeline that cleans and shapes data into reporting views, and dynamic Power BI dashboards sourced from those views decoupling reporting from the master DB.

Staging DB pulls raw data from the Oracle master; reporting DB holds refined, report-ready views.

SQLMesh + Dagster ETL performs quality checks, handles missing data and builds views.

Power BI dashboards consume the reporting views with rich filters and sorting.

Modular stages make the system easy for BI engineers to monitor and troubleshoot.

System Architecture

How the data flows

01

Oracle Master DB

Source of truth

02

Staging Mart

MySQL raw extract

03

ETL

SQLMesh + Dagster

04

Reporting Mart

Refined views

05

Power BI

Dynamic dashboards

Result 01

Secured the master database from unnecessary access and excessive read/write load.

Result 02

Standardised and orchestrated ETL with minimal human intervention.

Result 03

Gave stakeholders fast, reliable dashboards on cleaned, governed data.

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Further reading

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