Faire is a B2B marketplace helping independent retailers
ML to connect brands to retailers
Challenges include: search discovery & ranking, risk & fraud, seller success (lead scoring), incentive optimization
Year 1 (data team size: 1 → 1)
Main decisions
Online DB to use
BI tool
Data warehouse
Event recording
First real-time feature store for fraud
Data structure decisions can be very sticky, not easy to evaluate in the short term, a lot of critical and far-reaching decisions
Make sure to have cofounders or advisers that have worked with data systems
2 choices of DB: MongoDB vs MySQL
MongoDB pro: fast to set up and run
MySQL pro: data replication, consistency
Year 2 (data team size: 1 → 3)
Main decisions
Improved search system
Kubeflow vs Airflow for simple orchestration
First standardized core tables for analytics
So far, usage of GCP and Kubeflow seemed the most natural move
When scaling, decision making needs a more framework: create a rubric
Year 3 (data team size: 3 → 6)
Main decisions
Unify feature store
Migration to Airflow
Data quality monitoring
Experimentation platform
Events platform
Build / Buy experiment
We had a basic experiment system and we didn’t know what we were missing, so we tried to use an external solution
Took longer than expected and ultimately failed because of sync issues between our warehouse and our suppliers. Difficulty to map both data models.
Learnings: there is a cost in maintaining data consistency with external providers. Also, make sure that data infra decisions are tailored to your need.
Year 4 (data team size: 6 → 12)
Main decisions
Real-time ranking and low latency inference
Migration to Snowflake
Terraform everywhere
Snowflake migration from Redshifts
Thousands of tables to migrate, very painful over 6 months
Big data infra changes are hard, and necessitate a lot of coordination and project management to bring the whole company with you
No migration will be perfect, so set up an accuracy threshold to consider the job finished
Year 5 (2021, data team size: 12 → 30)
Main decisions
ML platform unification
Analytics unification
Make sure you have seasoned technical leaders to understand when it’s time to make big investments
Without feedback from DS and MLE and their workflow, you might not understand that there is a need to invest in analytics or ML platform
These DS projects are critical for the future
Summary
Make sure to have experimented folks in the early days
Close collaboration between eng and data team during the early days is crucial
Establish rubrics for decision making
Q&A
A concrete example of a data decision rubric?
Recently: does the data catalogue integrate with the BI tool? Can people follow tables to foster collaboration (”hey I notice you are an expert with this table…”)?
What data are you using?
Essentially customers’ behaviour and interaction with the marketplace
Analytics platform vs ML platform?
ML platform = unified feature store, unified ML training pipeline, model registry for later used, model serving (batch & real-time)
Analytics platform = how are we adding decisions, and who are adding them? All of these things need to be defined at once, in a precise place. Every single fact of the business is defined once, and everyone is using it.
Real-time feature store?
How grown, ranking and fraud system
Very interested in a unified feature store and understanding when to make the switch
Feature computation in real-time is a really hard topic, the cost of sync with an external provider is very real
Biggest growth challenges?
Hundred of small changes in the data analytics platform during internationalization, different currencies, when to make the conversion before computing the revenue?