How Vipra Software delivered a 62% total cost of ownership reduction by migrating a 2TB+ enterprise data estate from AWS Redshift to a serverless BigQuery + dbt architecture.
A financial services firm had accumulated over 2TB of critical business data in Amazon Redshift — a provisioned cluster architecture that had made sense years prior, but now represented a significant operational liability. The data team was fielding escalating cloud bills with no clear attribution, query performance was degrading as data volumes grew, and scaling required time-consuming manual cluster resizing with significant downtime risk.
The analytical workloads had also evolved significantly. What began as standard reporting had grown to include complex multi-table joins, advanced window functions, and ad-hoc analytical queries from 40+ business users across the organisation. The existing Redshift setup struggled under this expanding workload profile, and query queuing was becoming routine.
Engineering leadership needed a modernisation path that would not only address immediate cost concerns but also provide a future-proof foundation for growing data demands — without a "big bang" cutover that risked disruption to mission-critical reporting.
Vipra Software's solution architecture was designed around three core principles: serverless scalability, transformation modularity, and FinOps visibility from day one. We rejected a lift-and-shift approach in favour of a clean-sheet redesign that would address both cost and performance at their root causes.
The final architecture centres on BigQuery as the single analytical data store, fed by Cloud Composer (managed Apache Airflow) orchestrating a layered dbt transformation pipeline. Source data from operational databases is ingested via Fivetran connectors into a raw staging dataset in BigQuery, maintaining a complete historical record.
The dbt transformation layer is structured into three tiers: Bronze (raw source replicas with no transformation), Silver (cleaned, typed, and validated business entities), and Gold (aggregated, business-ready marts). This medallion approach ensures that analytical consumers always work from curated, tested data assets with full lineage visibility.
FinOps governance was embedded from the start. All BigQuery resources are tagged by business domain and cost centre, enabling granular cost attribution that was previously impossible with shared Redshift clusters.
The migration delivered the targeted 62% TCO reduction within the first billing cycle after cutover — exceeding the 55% target set at project inception. The $125K annual saving represented a full ROI on the project investment within 8 months.
Beyond direct cost savings, the engineering team reported a significant improvement in developer productivity. The dbt-native transformation layer reduced the time to add new analytical models from days to hours, and the introduction of automated data quality tests caught 3 data integrity issues in the first month that would previously have reached business users unchecked.
Query performance improvements were material across the board — the 12 most expensive analytical queries now run in an average of 8 seconds, compared to 4+ minutes on Redshift. This has enabled the finance team to run analyses interactively that previously required overnight batch jobs.