Production is going on.
US Bank Migrates 255 Data Views in 12 Days
A US bank retired TIBCO Data Virtualization before its fixed deadline while keeping 75 users, 20 Power BI reports and active data workloads running.
Client
Challenge
The bank had to remove a shared data layer without breaking the workloads that depended on it. That meant understanding what was actually in use, preserving transformation logic and replacing different workloads with technologies suited to how each one worked.
A single replacement pattern would have created unnecessary change. Integration and transformation processes needed one approach. Analytical logic belonged in Power BI. File-based processing required another design. Existing SQL Server targets and reports also represented proven assets that did not need to be replaced.
The bank therefore needed a migration approach that was fast enough for a 12-day cutover but controlled enough to prove that replacement outputs matched production results.
A single replacement pattern would have created unnecessary change. Integration and transformation processes needed one approach. Analytical logic belonged in Power BI. File-based processing required another design. Existing SQL Server targets and reports also represented proven assets that did not need to be replaced.
The bank therefore needed a migration approach that was fast enough for a 12-day cutover but controlled enough to prove that replacement outputs matched production results.
Solution
The bank and BCT used a workload-specific migration model. Integration and transformation processes moved to Microsoft SQL Server Integration Services. Analytical transformation logic moved into Power BI semantic models. File-based processing was redesigned with Python-based pipelines. The existing SQL Server target environment and Power BI reports were retained.
Recurring manual processing and file-download activities were replaced with automated ingestion and scheduling. Data outputs were validated through like-for-like reconciliation, parallel processing and business-user acceptance testing.
The programme also reviewed planned deployments against current business usage. This ensured that only actively required solutions moved into production rather than reproducing every historical workload simply because it existed.
BCT contributed across assessment, solution design, development, testing, documentation and knowledge transfer. Clear documentation and concurrent work across teams allowed the migration to progress under the compressed schedule.
Recurring manual processing and file-download activities were replaced with automated ingestion and scheduling. Data outputs were validated through like-for-like reconciliation, parallel processing and business-user acceptance testing.
The programme also reviewed planned deployments against current business usage. This ensured that only actively required solutions moved into production rather than reproducing every historical workload simply because it existed.
BCT contributed across assessment, solution design, development, testing, documentation and knowledge transfer. Clear documentation and concurrent work across teams allowed the migration to progress under the compressed schedule.
Impact
The bank successfully migrated 100% of the active workloads requiring replacement. The core migration finished in 12 days, before the 27 November 2025 decommissioning deadline. Validation, user acceptance and stabilization continued through 15 December 2025.
Approximately 75 business users and 20 Power BI reports continued to be supported. No production downtime was reported during the transition.
The bank also reported a 75% reduction in failures and incidents. Recurring manual data-processing and file-download activities were eliminated.
One Python-based workflow reduced processing time from approximately four hours to around five minutes, an estimated 98% reduction. The recurring monthly full-refresh and historical-backup activity was also removed.
The bank retained its existing Power BI reports without significant redesign and protected data accuracy through like-for-like comparison with production outputs. The TIBCO platform could then be retired in favour of technologies already established within the organisation.
Approximately 75 business users and 20 Power BI reports continued to be supported. No production downtime was reported during the transition.
The bank also reported a 75% reduction in failures and incidents. Recurring manual data-processing and file-download activities were eliminated.
One Python-based workflow reduced processing time from approximately four hours to around five minutes, an estimated 98% reduction. The recurring monthly full-refresh and historical-backup activity was also removed.
The bank retained its existing Power BI reports without significant redesign and protected data accuracy through like-for-like comparison with production outputs. The TIBCO platform could then be retired in favour of technologies already established within the organisation.
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