> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/integrations/third-party-integrations/integrate-with-databricks.md).

# Integrate with Databricks

Databricks is where many teams train demand forecasting models and build AI systems - and both jobs need verified real-world context in the lakehouse. PredictHQ data lands in Databricks through two natively supported paths, both continuously updated and neither requiring a custom pipeline:

* **AWS Data Exchange to S3** - full and incremental exports [delivered to S3](/integrations/third-party-integrations/aws-data-exchange.md), which Databricks reads directly.
* **Snowflake shared tables** - a [Secure Data Share](/integrations/third-party-integrations/snowflake.md) queried from Databricks over the native Snowflake connector.

[Get in touch](https://www.predicthq.com/contact) with us to set up delivery via ADX or Snowflake. PredictHQ can also deliver Features API output per Beam Analysis through the same channels, so model-ready features arrive alongside the event records.

Once landed, the data serves each integration path:

* **Model training** - join event features to your demand history and train in your existing Databricks ML workflow. Features are keyed by a `beam.analysis_id`, so feature selection reflects what actually drives demand at each location.
* **Inference** - retrieve future-dated features at every forecast run, whether your models are bespoke or [pre-trained time series foundation models](/getting-started/guides/features-api-guides/using-event-features-with-time-series-foundation-models.md) consuming them as covariates.
* **Internal grounding** - the event tables double as the retrieval corpus for LLMs and agents built on Databricks, so AI systems answer from verified real-world context governed by your own platform - see [internal grounding](/integrations/integration-guides/internal-grounding.md).

Databricks resources for reading data from S3 or Snowflake:

{% embed url="<https://docs.databricks.com/en/storage/amazon-s3.html>" %}

{% embed url="<https://docs.databricks.com/en/external-data/amazon-s3-select.html>" %}

{% embed url="<https://docs.databricks.com/en/external-data/snowflake.html>" %}


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