Tinuiti builds a scalable data lake for AI-driven marketing with Fivetran
Tinuiti's previous data architecture was riddled with manual processes: engineers spent up to 150 hours per month maintaining API connectors, customer onboarding took up to 6 weeks, and the old managed data warehouse limited the team to serving only 20% of customers while suffering from Amazon Redshift performance bottlenecks and fragmented, inconsistent schemas.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Client pipeline setup
A client is onboarded by filling in 2 fields in Fivetran's setup form to connect up to 80 platforms.
Fivetran reduced pipeline setup time from 2-4 weeks to under an hour (120x acceleration), eliminated at least 80% of manual maintenance work, enabled self-service for 80% of data connectors, and allowed Tinuiti to serve its entire customer base—up from 20%—while powering AI and ML-driven marketing analytics and forecasting.
What failed first
The previous managed data warehouse lacked full data access and control, produced fragmented platforms with inconsistent schemas, and required manually managing in-house pipelines with constant schema changes that proved unsustainable.