Marketing ops · Production

Tinuiti builds a scalable data lake for AI-driven marketing with Fivetran

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Client pipeline setup
trigger
“I can fill in 2 fields in Fivetran's setup form and onboard a client on up to 80 platforms”
2
Multi-source automated ingestion
integration
“library of 700+ connectors to seamlessly integrate data from 100+ different marketing platforms and data sources into Amazon S3”
3
Apache Iceberg format conversion
integration
“Tinuiti uses Fivetran to automatically convert data into Apache Iceberg format for compliant querying, eliminating the need for manual format conversions”
4
AWS Glue catalog population
integration
“With Fivetran automatically populating the AWS Glue catalog, Tinuiti strengthened data governance and security while scaling back operational overhead”
5
Query-ready data in Amazon S3
output
“teams can query data directly from Amazon S3, send structured data to a warehouse, or feed ML models without duplicating datasets”
6
AI/ML analytics and forecasting
ai_action
“AI and ML-powered analytics provide forecasting capabilities, helping brands predict future campaign performance and ROI with greater accuracy”
Reported outcome

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.

Reported metrics
Client onboarding acceleration120x
Pipeline setup timefrom 2-4 weeks to under an hour
Self-service data connectors80%
Manual pipeline maintenance eliminatedat least 80%
Show all 10 reported metrics
client onboarding acceleration120x
pipeline setup timefrom 2-4 weeks to under an hour
self-service data connectors80%
manual pipeline maintenance eliminatedat least 80%
API connector maintenance overhead (before)up to 150 hours per month
customer onboarding time (before)up to 6 weeks
customers previously served20%
media spend managednearly $4 billion
available connectors700+
marketing platforms integrated100+
Reported stack
FivetranFivetran APIAmazon S3Apache IcebergAWS Glue
Source
https://www.fivetran.com/case-studies/tinuiti-builds-a-scalable-data-lake-for-ai-driven-marketing
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 ser…

What tools did this team use?

Fivetran, Fivetran API, Amazon S3, Apache Iceberg, AWS Glue.

What results were reported?

Client onboarding acceleration: 120x; Pipeline setup time: from 2-4 weeks to under an hour; Self-service data connectors: 80%; Manual pipeline maintenance eliminated: at least 80% (source-reported, not independently verified).

What failed first in this deployment?

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 pro…

How is this marketing ops AI workflow structured?

Client pipeline setup → Multi-source automated ingestion → Apache Iceberg format conversion → AWS Glue catalog population → Query-ready data in Amazon S3 → AI/ML analytics and forecasting.

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