Technology & data infrastructure
AIT
Real-time data warehousing and machine learning platform
Measured results
−70%
query latency
+50%
analytics accuracy
+40%
customer behaviour prediction
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 6
The problem
A legacy SQL Server estate could not support real-time analytics or machine-learning-based decisions at the scale the business had reached.
What was built
Migration to a cloud warehouse with change-data-capture streaming, and custom models deployed on managed infrastructure.
Technical approach
- Migration
- SQL Server to Google BigQuery, with Kafka Connect carrying change data capture.
- Kubernetes
- A cluster hosting the Kafka installation so ingestion scales independently of query load.
- Machine learning
- Custom models built and served on Google Vertex AI.
Measurement framework
Deployment evidence is drawn from the published engagement. The delivery and measurement plans are case-specific recommendations grounded in the documented solution and results.
- Evidence status
- The source documents a working real-time data and machine-learning platform, supported by reported improvements in query performance and analytics accuracy.
- Indicative delivery plan
- Benchmark the existing warehouse, introduce the streaming layer, migrate priority workloads, validate models on a frozen dataset, then complete a phased production cutover.
- Recommended baseline
- Recommended: median and 95th-percentile query latency on a fixed production-representative benchmark, plus model accuracy on a frozen, labelled validation set.
- Recommended measurement method
- Run the same query and model benchmark before and after migration at equivalent concurrency and data volume. Version the dataset and code, separate infrastructure gains from model gains, and have the data owner sign off the comparison.
Recommended items are not presented as the original study method. They should be replaced with the confirmed project methodology when the underlying records are approved for publication.