3 days → minutes
“We finally trust the dashboards. Reporting that used to take three days at end-of-month now updates in minutes finance and growth use the same numbers.”
Cloud Data Warehouse
A Warehouse Built to Run Your Business
Your warehouse is the foundation of every decision, every dashboard, and every downstream AI feature. We architect, build, and optimise warehouses in BigQuery, Microsoft Fabric, and Snowflake sized for cost, speed, and the way your team actually queries.
−45%Average warehouse spend
10×Faster complex queries
100%Owned by you
Modern stack. Right-sized.
BigQuery, Microsoft Fabric, and Snowflake each have a sweet spot and a footgun or two. We help you pick the right one for your data volume, query patterns, and existing investments (especially if you're a Microsoft shop). Then we set it up properly: project structure, access controls, partitions, and cost guardrails.
- Right-sized for your actual query patterns
- Security and access controls from day one
- Cost monitoring and budgets wired in
- Migration path off legacy systems, planned up front
What's Included
Architecture, Migration, And Tuning
From greenfield to legacy lift-and-shift handled.
Warehouse Architecture
Project / schema layout, partitioning, clustering, and access controls that scale with your team.
- Project / workspace structure
- Partitioning and clustering strategy
- Role-based access controls
- Environments (dev / staging / prod)
BigQuery / Fabric / Snowflake
Native expertise in all three and an honest recommendation on which fits your stack.
- BigQuery slot reservations
- Microsoft Fabric OneLake design
- Snowflake warehouse sizing
- Multi-cloud and hybrid setups
Migration from Legacy
From Redshift, SQL Server, MySQL, on-prem Hadoop we've done it. Zero-downtime where it matters.
- Discovery and source inventory
- Cut-over and dual-write strategy
- Backfill and reconciliation
- Decommissioning of legacy systems
Cost & Performance Tuning
Most warehouses we audit have 30–50% of spend optimisable. We find it, prove it, and ship the fixes.
- Query performance analysis
- Slot / warehouse sizing reviews
- Materialised views and aggregations
- Budgets and alerts
How We Work
Typical Engagement, Week by Week
Predictable timelines, clear handoffs.
01
Discovery
We profile current state: sources, query patterns, spend, users, governance. Output: a target architecture and roadmap.
02
Foundation
We stand up the warehouse, environments, access controls, and the first production-grade dataset.
03
Migration or Build
Either migrate from legacy in waves, or build out the mart layer that powers your BI and AI.
04
Operate & Tune
We monitor cost and performance, ship optimisations, and hand over a fully documented platform.
Why GrowMos
Vendor-Agnostic, Architect-Led
The right warehouse, not the one we get paid for.
Tool-Agnostic
We're certified across BigQuery, Fabric, and Snowflake and we'll tell you honestly which fits your stack and budget.
Cost Engineers
Most warehouses have 30–50% optimisable spend. We find it in the first audit, then ship the fixes.
Security-First
Access controls, PII handling, and audit logs are designed in from day one not bolted on.
Knowledge Transfer
We leave your team with documentation, runbooks, and pairing sessions not a black box.
Selected Outcomes
Numbers from real engagements
The metrics below come from named clients in B2B SaaS, e-commerce, and logistics. Names and figures published with their consent.
18 sources → 1 source
“They rebuilt our warehouse from scratch in 4 weeks. We went from 18 disconnected spreadsheets to one place we actually query and our finance close dropped from 10 days to 4.”
ROAS up 41%
“Our paid media decisions used to be guesses. Now we know exactly which campaigns are profitable and which to cut. ROAS up 41% in a single quarter.”
Sales–data parity, day 1
“Reverse ETL into HubSpot changed everything. Sales finally sees the same lifecycle stage as the data team no more 'whose numbers are right' arguments.”
Good to Know
Frequently Asked Questions
Which warehouse should I pick?
It depends on your existing cloud investments, query patterns, and team skills. Microsoft shops usually win with Fabric. Pure-GCP ecosystems usually win with BigQuery. Heavy ELT + lots of complex joins often favours Snowflake. We'll walk through the trade-offs on a discovery call.
Can you migrate us off Redshift or SQL Server?
Yes we've done it for SaaS and E-commerce clients on data sets from 100GB to 30TB+. The migration is staged in waves with a dual-write / dual-read window so nothing breaks for downstream consumers.
How long does a typical warehouse build take?
Greenfield on BigQuery or Snowflake: 4–6 weeks to first production dataset. Migration: 8–12 weeks depending on source complexity. We give you a fixed timeline after discovery.
Do we need a separate BI tool?
Each warehouse has its own built-in insights tool, but for executive dashboards and self-serve analytics most teams pair with Power BI or Metabase. We can build either see our BI service page.
Let's design the right
warehouse.
Book a free discovery call. We'll profile your current state, sketch a target architecture, and tell you honestly what it costs and how long it takes.