Why Integrate Presenc AI with Databricks
Databricks has become the lakehouse standard for marketing data at the enterprise level. MMM, custom Bayesian inference, and large-scale marketing analytics increasingly run on Databricks-resident Delta tables. AI visibility data needs to join the lakehouse alongside the rest of marketing data infrastructure.
The Presenc AI and Databricks integration writes weekly LLM share of voice, citation frequency, and brand mention data directly into Databricks Delta tables. The data feeds MMM workflows, Mosaic AI custom models, MLflow experiments, and any downstream analytics in the Databricks ecosystem.
Setup Guide
Step 1: Configure Databricks credentials. In Presenc AI, navigate to Settings, Integrations, Databricks. Provide the Databricks workspace URL, service principal, catalog, schema, and target Delta tables. Presenc creates the tables automatically with appropriate partitioning.
Step 2: Pick refresh mode. Batch refresh (weekly) for MMM workflows. Streaming refresh (continuous) for real-time dashboards and tactical monitoring. The mode affects the Delta table architecture; pick based on consumption needs.
Step 3: Set up the Unity Catalog governance. The Presenc tables register in Unity Catalog for fine-grained access control. Marketing science teams can have read access to the raw tables; downstream teams can have access only to the derived MMM-ready marts.
Step 4: Build the MMM data mart with Delta Live Tables. DLT pipelines transform raw Presenc data into the channel-aligned MMM mart. The DLT approach handles the incremental refresh, data quality checks, and lineage tracking that production MMM workflows require.
Step 5: Run MMM on the lakehouse. PyMC-Marketing, LightweightMMM, and custom Bayesian models run natively on Databricks. The Presenc data input enters the model with no additional shuffling.
Use Cases
Lakehouse MMM. Marketing science teams running MMM on Databricks benefit from the unified data plus compute architecture. Delta-resident Presenc data joins the MMM workflow without warehouse-to-compute data movement.
Mosaic AI custom inference. Databricks Mosaic AI builds custom models against lakehouse data. AI visibility as a feature in propensity models, churn prediction, and content optimization benefits from native integration.
MLflow experiment tracking. MMM refits and methodology changes track in MLflow with full lineage. Methodology audit trails, version comparisons, and reproducibility benefit from MLflow integration.
Key Features
| Feature | Benefit |
|---|---|
| Native Delta tables | Lakehouse-native storage with ACID and time-travel |
| Unity Catalog integration | Fine-grained access control and lineage tracking |
| Streaming + batch modes | Match cadence to consumption pattern |
| Delta Live Tables compatible | Incremental refresh and data quality checks |
| MLflow integration | Methodology version tracking for MMM |
| Multi-language access | SQL, Python, R, Scala all natively supported |