# A cloud data lakehouse for an online vehicle-trading platform

> An online automobile-trading platform · SaaS & Tech

A cloud-based data lakehouse consolidated siloed sources into a single relational store, enabling business intelligence and analytics across a fast-growing vehicle-trading platform.

## At a glance

- Engagement: A few weeks

## What was the problem?

An online vehicle-trading platform captured data right across the business, but analysis was siloed by system and team. Data lived across multiple platforms and formats, including relational databases, NoSQL stores and spreadsheets, while the platform's business-intelligence and advanced-analytics ambitions depended on relational data.

## What did QuantSpark do?

QuantSpark designed and rapidly built a cloud-based data lakehouse on a scalable cloud architecture, integrating and centralising the disparate sources into a single relational cloud data warehouse. The future-proof, low-maintenance design suited the company's fast-moving development culture and produced a demonstrable product within weeks.

## What changed?

Analysis is now possible across previously siloed datasets, independent of their original platform or format. Business users can interrogate the centralised data through self-service business-intelligence tools with minimal technical training, and the scalable design keeps operational overheads low as the platform grows.

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Canonical page: https://quantspark.ai/case-studies/automotive-marketplace-data-lakehouse
More about QuantSpark: https://quantspark.ai/llms.txt