Cloud Data Warehouse Migration – Case Study
- Warehouse Migration
- Teradata Modernisation
- Cloud Data Platform
- Data Engineering
Project Snapshot
Client
Specialty Chemicals Manufacturer
Location
Global operations across 4 regions
Industry
Specialty Chemicals Manufacturing
Services
- Teradata Modernisation
- Warehouse Migration Rescue
- Cloud Data Platform Migration
1. Introduction
A specialty chemicals manufacturer was nineteen months into a warehouse migration that had stopped moving. The original plan was to move a fourteen-year-old Teradata enterprise data warehouse to a cloud platform within fourteen months. Instead, the programme had crossed nineteen months, run 2.1 times over budget, missed three cutover dates, and reached a point where the steering committee froze progress.
The board had been told the issue was technical complexity. DataTheta’s assessment showed something different. The migration was not failing because the technology could not work. It was failing because the plan had been built before anyone fully understood the estate.
2. Business Context
The client operated across specialty chemicals manufacturing, with plants, regions, pricing rules, cost allocations, supply chain processes, and regulatory reporting all dependent on the legacy warehouse. The Teradata estate had grown over fourteen years and had become deeply embedded in the business.
On paper, the estate included around 3,100 stored procedures and nearly 900 ETL jobs. In practice, the real environment was larger and less documented. Business logic was buried inside scripts, procedures, and transformation flows. Many of the engineers who originally built those rules had moved on, leaving the company with a migration plan that underestimated both technical dependency and business risk.
The goal was not simply to move workloads to the cloud. The goal was to exit Teradata safely, reduce cost, preserve trusted reporting, and avoid carrying legacy inefficiency into a consumption-priced cloud model.
11 Months
Full Decommission
31%
Estate Retired
£1.9M
Annual Savings
6 Waves
Evidence Cutover
3. The Challenge
The programme had been treated as a straightforward migration, but the estate was not straightforward. Discovery had been budgeted at only four weeks, which was far too short for a warehouse of this age and complexity. As a result, the original timeline was based on assumptions instead of evidence.
Automated lineage scanning later found hundreds of undocumented procedures still active in production, including some tied to regulatory reporting. Pricing rules, cost allocations, and plant-level exceptions were embedded inside ETL scripts rather than documented in a governed business layer. The migration approach was also largely lift-and-shift, meaning the old Teradata structure was being recreated almost directly on cloud.
That would have moved the problems, not solved them. The business was on track to receive a cloud warehouse that cost more, performed no better, and remained difficult to govern.
4. The Strategic Reframe
DataTheta did not restart the migration. That would have wasted nineteen months of partially useful work. Instead, the programme was reframed around what the discovery actually revealed.
4.1) Discovery Before Commitment
The first priority was to stop the cutover and complete proper discovery. DataTheta extended discovery, mapped procedures, dependencies, lineage, and downstream consumers, and created the first complete view of the warehouse estate.
4.2) Redesign Before Migration
The second priority was to stop treating the estate as one block to be moved wholesale. Every workload had to be classified based on business value, technical complexity, usage, and cost impact before migration decisions were made.
5. The DataTheta Solution
DataTheta rebuilt the programme around evidence, segmentation, semantic reconstruction, and wave-based migration.
5.1) Estate Inventory
Automated scanning identified every active procedure, dependency, downstream report, and data consumer. This revealed that the known estate was incomplete and that undocumented production logic had been missed in the original plan.
5.2) Workload Classification
DataTheta classified the procedures into four groups: retire, rehost, redesign, and reconstruct. Dead code and unused workloads were retired instead of migrated. Straightforward workloads were rehosted. High-cost, high-complexity workloads were redesigned for cloud. Undocumented business logic was reconstructed before movement.
5.3) Semantic Layer Reconstruction
Critical pricing rules, cost allocations, and plant-level exceptions were moved out of buried ETL scripts and into a governed semantic layer. This made key business logic documented, owned, auditable, and reusable.
5.4) Wave-Based Migration
The migration was executed in six waves, starting with lower-risk workloads and leaving regulatory reporting for last. Each wave ran in parallel against Teradata until numbers reconciled for two full close cycles. No cutover happened on assumption. Every cutover happened on evidence.
6. Implementation Approach
The engagement followed a rescue-first approach designed to recover value without pretending the earlier overrun could be erased.
6.1) Assess
DataTheta completed the discovery that had been skipped at the start. The real estate size, hidden dependencies, and undocumented business logic were mapped before the plan was finalised.
6.2) Segment
The estate was split by business value and migration path. This prevented unnecessary workloads from being moved and helped focus engineering effort where redesign actually mattered.
6.3) Rebuild
The most important business logic was reconstructed into a governed semantic layer, reducing reliance on undocumented scripts and individual knowledge.
6.4) Decommission
After wave-based migration and reconciliation, the legacy Teradata system was powered down. The programme ended with a smaller, documented, governed, and more forecastable cloud warehouse estate.
7. Business Impact
The rescue changed the outcome of the migration. The Teradata system was fully decommissioned eleven months after the programme was re-planned. Nearly a third of the estate was retired instead of migrated, reducing complexity and avoiding unnecessary cloud cost.
The finance team also stopped maintaining a shadow reconciliation spreadsheet, which became an important sign of trust. They had trusted the old numbers. After the migration, they trusted the new ones.
The total programme cost still exceeded the original approved budget because the programme had already started badly. DataTheta’s role was not to make a failed programme cheap. It was to make it recoverable, governed, and worth finishing.
8. Conclusion
This case shows why warehouse migration success depends on honest discovery before commitment. The platform was not the real problem. The real issue was a fixed migration promise made before the estate was understood. By completing discovery, retiring unnecessary workloads, reconstructing business logic, and migrating in controlled waves, DataTheta helped the client turn a frozen Teradata exit into a governed cloud warehouse programme that finally landed.
“The uncomfortable part was being told that the four weeks of discovery we had signed off at the start was the reason we were nineteen months late. Nobody wants to hear that. But it was true, and once we accepted it, the path forward was obvious.”
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