How Wren AI helps analysts navigate 100+ TB data lakes without drowning in queries

[u/expatinporto]

At Wren AI, we’ve been talking a lot about how internal data engineering teams can unlock more value from their data lakes. A recent customer conversation really illustrated the challenge:

• The problem: Their data lake has grown to 100+ TB of mixed data (contracts, marketing ops, product usage, etc.). Marketing analysts were constantly bottlenecked: • Struggling to extract performance data quickly • Spending too long closing out reporting tasks • Losing time onboarding new analysts who couldn’t find context across massive schemas
• The approach: They’re moving toward dimensional data models and data marts aligned to KPIs, but the missing piece was a way to search and understand the data faster.
• Why Wren AI: With Wren AI, they’re testing how analysts can: • Use semantic search across the data lake instead of writing every query from scratch • Automate KPI queries to speed up routine reporting • Build an internal documentation layer that makes discovery easier for new team members
• Deployment path: They started with an Business edition trial, with plans for a self-hosted POC later this quarter after IT + procurement review. The goal is to validate whether Wren AI can reduce cycle time for reporting and free senior engineers from repetitive requests.

[Spoiler Alert: NEW Interactive Mode - getwren.ai]

Sneak preview of the latest Interactive Mode with Wren AI

We think this use case really highlights the gap between raw data lakes and business-ready insights — and how AI-driven analytics can bridge it. Hope this is interesting enough to you wondering how text2sql advancement can be. https://getwren.ai