Accelerated Oracle migration delivery by using a wave based plan, automated testing, and parallel runs for critical payment domains
Reduced reporting friction by aligning stakeholders on a single set of definitions and publishing a governed semantic layer for consistent metrics
Improved reliability by introducing monitoring, data quality checks, and a clear release process for pipelines and models
Solution
Datasphere Dynamics delivered a controlled migration from Oracle to Snowflake with a refactor first approach.
We began by mapping the payments reporting landscape and grouping assets into migration waves based on business criticality and cutover risk. We introduced automated reconciliation checks so every migrated dataset could be validated for completeness and accuracy.
To modernize analytics, we rebuilt transformations using consistent modelling standards, embedded testing, and documentation. We then delivered a governed BI layer with agreed KPI definitions to remove reporting inconsistency across teams.
Alongside the build, we introduced practical operating standards for quality, monitoring, and releases so the platform remained reliable after cutover.
Challenge
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The payments function needed to modernize a reporting estate that had grown complex over time. Critical payment and event data was stored and reported from Oracle with transformation logic spread across stored procedures, scheduled jobs, and manual steps.
Operational and control teams relied on similar reports with different definitions, which created rework and reduced trust. Change cycles were slow because reporting and transformation logic were tightly coupled, and validation was time consuming during month end and audit periods.
The team needed a migration approach that reduced risk, preserved auditability, and delivered improved performance without interrupting daily operations.
The model
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Oracle
AWS (S3)
Snowflake
dbt
Power BI
Technology used:
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5 dedicated team members
Full time resources:
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Project duration:
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9 Months