An alternative-asset market-data platform
AWS-validated reference — full details available to AWS Partner Validation or on request. (Customer name held on file with VeUP.)
VeUP advised the customer (product Orbis), an alternative-asset market-data platform, on migrating its analytics layer from Amazon Redshift to Amazon Athena — serverless query over S3-resident data — cutting roughly 30% of analytics cost on a cleaned, structured alternative-asset dataset.
The challenge
the customer (product Orbis) collects alternative-asset market data by scraping unstructured auction sources and standardizing it into a structured dataset. Its analytics layer ran on Amazon Redshift, where provisioned-warehouse cost grew faster than query value for a spiky, R&D-heavy workload. The team needed to reduce analytics cost without losing the ability to run ad-hoc queries over a growing, S3-resident data lake, and to clean noisy scraped inputs into an analysis-ready dataset.
The solution
VeUP advised migrating the analytics layer from a provisioned Amazon Redshift warehouse to serverless query with Amazon Athena over S3-resident data — paying per query rather than for idle warehouse capacity. The scraping-and-standardization pipeline produces a clean, structured alternative-asset dataset on Amazon S3 that Athena queries directly, with the stated roadmap of deploying AI models on top of the curated dataset.
Production outcomes
| KPI | Result |
|---|---|
| Production outcomes | The Amazon Redshift-to-Amazon Athena migration delivered roughly 30% analytics-cost reduction (customer-stated, captured in the VeUP engagement record), and produced a standardized, cleaned structured alternative-asset dataset from unstructured scraped inputs. Presented as a historical example implementation — the account has since churned. |
| Engagement window | 2024-04-10 (AWS Resell Customer Live) → 2024-04-10 (delivered); account since churned — historical example |
| Cost / TCO posture | The migration was cost-driven: moving from a provisioned Redshift warehouse (paying for idle capacity) to serverless Athena (pay-per-query over S3) removed the fixed warehouse cost floor for a spiky analytics workload — the source of the ~30% analytics-cost reduction. |
| Lessons & continuation | For spiky, ad-hoc analytics over an S3-resident lake, serverless Athena beats a provisioned warehouse on unit economics; data standardization/cleaning of noisy scraped inputs is the precondition for both reliable analytics and any downstream AI build. |
Architecture
A Well-Architected-annotated view of the previous-state provisioned Amazon Redshift warehouse, the target-state serverless Amazon Athena query layer over the S3-resident structured dataset, and the migration moves between them.
Previous state
Target state on AWS
Layer-by-layer build-up
Full Well-Architected overview
Amazon Athena · Amazon S3 · Amazon Redshift (migrated off)