RDSolutions empowers retail pricing intelligence with Databricks
RDSolutions migrated its legacy Azure data warehouse pipeline to the Databricks Data + AI Platform, using Delta Lake, Unity Catalog and Databricks Assistant to process billions of retail pricing data points, cutting a data validation job from 4 days to 3 hours, a 97% improvement.
Overview
RDSolutions migrated its legacy Azure data warehouse pipeline to the Databricks Data + AI Platform, using Delta Lake, Unity Catalog and Databricks Assistant to process billions of retail pricing data points, cutting a data validation job from 4 days to 3 hours, a 97% improvement.
This entry has 13 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
RDSolutions relied on an Azure data warehouse that failed to scale cost-efficiently or effectively; big queries forced data teams to stop other work, and a complex aggregation job failed to complete, signaling the legacy pipeline could not meet leadership's ambitions for scale.
The solution
RDSolutions migrated to the Databricks Data + AI Platform, making Delta Lake the backbone of its data strategy for structured and unstructured data, using Unity Catalog for a single data governance and access-control model, Databricks SQL for self-service analyst queries, and Databricks Assistant to automate development tasks like error troubleshooting and table documentation.
Reported business value
A data validation job that took four full days in the legacy warehouse was reduced to seven hours immediately after switching to Databricks, and to just three hours after optimizing the pipeline—a 97% process and performance improvement directly impacting the speed at which RDSolutions delivers accurate and reliable customer data.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other retail entries in the register.
Omoda's generative AI stylist lifts conversion rate 2.5x for online fashion shoppers
Dutch fashion retailer Omoda built AI stylist, a virtual shopping assistant powered by Google Cloud's Gemini Pro 1.5 model with Vertex AI Multimodal Embeddings and Vector Search. AI stylist holds natural-language conversations with customers, understands their needs, and builds complete outfits combining clothing, shoes, and accessories rather than just matching similar products, with an Intelligent Brand Matching feature that factors in brand affinities. Customers who use AI stylist convert at a rate 2.5 times higher than average, and the tool also improves inventory management through better trend anticipation.
Furniture.com Transforms Online Search with Databricks
Furniture.com unifies over 60 retail partners and 1.5 million SKUs on the Databricks Data + AI Platform, using Delta Lake, MLflow and Unity Catalog to run its ML lifecycle. Its Find It AI product-discovery tool uses generative AI to create a synthetic image representing shopper intent, then matches it against the product catalog for image-based search. A Collections model uses LLMs to automatically group related products, finding more than 16,000 collections across 50+ partners with no human intervention. Users who interact with Find It AI show a click-through rate 8x higher than baseline and a return rate 3.2x higher than baseline.
Furniture.comDatabricks Data + AI Platform · Delta Lake · MLflow +3
How Walmart Achieved Enterprise Transformation Through Digital Experience Innovation
Working with implementation partner Nativa on LivePerson's technology, Walmart customized and integrated conversational technology into its WhatsApp channel, including a FAQ-Transactional Bot to streamline searches, inquiries and purchases. The solution delivered a 60% increase in productivity and a 10 percentage point improvement in CSAT/NPS, along with operational savings through automated customer service processes.
WalmartLivePerson · WhatsApp
Zalando enhancies customer engagement and operational efficiency with Mistral
Zalando, a European e-commerce platform, integrated Mistral models hosted on AWS Bedrock into its platform to add natural language processing and machine learning capabilities. The integration supports personalized recommendations, improved customer service and streamlined operations for the retailer's shopping experience.
ZalandoMistral · Amazon BedrockWas this helpful?
Your feedback helps us improve our use case database