Markin + Databricks
Lakehouse tables and feature stores as first-class signal.
Where a data science team already exists, Markin should extend it rather than duplicate it. Existing features and models become inputs to the hypothesis space instead of being rebuilt.
Attribute write-backRead only
What Markin reads
- Delta tables and Unity Catalog assets
- Existing feature store definitions
- Model outputs your team already produces
What Markin writes back
- Decision and holdout Delta tables
- Per-experiment causal reads
How the connection works
| Pattern | What it means here |
|---|---|
| Attribute write-back | Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval. |
| Read only | Markin reads signal from this system. Nothing is written back and no schema is changed. |
Databricks questions
- How does Markin connect to Databricks?
- Attribute write-back, Read only. Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval.
- What does Markin read from Databricks?
- Delta tables and Unity Catalog assets; Existing feature store definitions; Model outputs your team already produces.
- What does Markin write back into Databricks?
- Decision and holdout Delta tables Per-experiment causal reads
- Do we have to move our data to Markin?
- No. Markin reads from your warehouse, product events and operational systems in place, on your compute, under the access rules your data team already set. Nothing is copied into a separate customer base and there is no vendor-side profile store to migrate off later.
- Does Markin replace our engagement platform or CDP?
- No, and it should not. Your engagement platform keeps the channel, the templates, the deliverability and the governance. Your CDP keeps identity and consent. Markin adds the layer neither has: deciding which action deserves to exist for each customer, and proving it against a holdout.
- What if the system we use is not listed?
- The four activation patterns cover almost everything: attribute write-back, triggered event, decision API and direct surface rendering. Any system that exposes an API, accepts a table, or can read a warehouse column can receive decisions. New connectors are built during deployment, typically in days.