Databricks Community

vamsi_simbus · 4 weeks ago

Hi Databricks Community,

I’m looking for information on the partnership between Databricks and Kinaxis. Specifically:

Are there any official integrations or joint solutions available between the two platforms?
Does Databricks provide any accelerators, reference architectures, or connectors to work with Kinaxis (especially around supply chain or demand planning use cases)?

Any documentation links, example implementations, or partnership details would be greatly appreciated.

Thanks in advance for your help!

Louis_Frolio · 4 weeks ago

Greetings @vamsi_simbus , I did some digging and have some helpful information for you.

Here’s a concise summary of what’s publicly available today on Databricks + Kinaxis.

A formal strategic partnership between Databricks and Kinaxis was announced on April 1, 2025, to combine the Kinaxis Maestro platform with the Databricks Data Intelligence Platform for AI-powered supply chain orchestration.
Public materials highlight Delta Sharing as the secure, cross‑platform data sharing mechanism enabling governed, zero‑copy exchange of datasets between platforms as part of the joint solution.
Delta Sharing is Databricks’ open protocol for secure data sharing (Databricks‑to‑Databricks and open sharing) with detailed documentation on setup, governance, and consumption patterns.
Kinaxis describes Maestro as an AI‑infused, end‑to‑end supply chain orchestration platform with a supply chain data fabric that ingests internal/external sources—aligned with the data unification goals of the partnership.

Databricks provides multiple Solution Accelerators relevant to supply chain and demand planning, including Demand Forecasting at Scale, Safety Stock, and Supply Chain Distribution Optimization (with notebooks and implementation guides).
Databricks publishes reference architectures for supply chain, including a Manufacturing Digital Supply Chain blueprint (integrations, lakehouse patterns, real‑time visibility, scenario planning, and agentic AI), and retail demand forecasting architecture.
From a connector standpoint, the public guidance for Databricks ↔ Kinaxis emphasizes using Delta Sharing and governed data exchange rather than a dedicated “Kinaxis connector” authored by Databricks. That integration pattern is explicitly called out in the partnership press releases and supported by Databricks documentation.

Hope this helps, Louis.

Louis_Frolio · 4 weeks ago

Greetings @vamsi_simbus , I did some digging and have some helpful information for you.

Here’s a concise summary of what’s publicly available today on Databricks + Kinaxis.

A formal strategic partnership between Databricks and Kinaxis was announced on April 1, 2025, to combine the Kinaxis Maestro platform with the Databricks Data Intelligence Platform for AI-powered supply chain orchestration.
Public materials highlight Delta Sharing as the secure, cross‑platform data sharing mechanism enabling governed, zero‑copy exchange of datasets between platforms as part of the joint solution.
Delta Sharing is Databricks’ open protocol for secure data sharing (Databricks‑to‑Databricks and open sharing) with detailed documentation on setup, governance, and consumption patterns.
Kinaxis describes Maestro as an AI‑infused, end‑to‑end supply chain orchestration platform with a supply chain data fabric that ingests internal/external sources—aligned with the data unification goals of the partnership.

Databricks provides multiple Solution Accelerators relevant to supply chain and demand planning, including Demand Forecasting at Scale, Safety Stock, and Supply Chain Distribution Optimization (with notebooks and implementation guides).
Databricks publishes reference architectures for supply chain, including a Manufacturing Digital Supply Chain blueprint (integrations, lakehouse patterns, real‑time visibility, scenario planning, and agentic AI), and retail demand forecasting architecture.
From a connector standpoint, the public guidance for Databricks ↔ Kinaxis emphasizes using Delta Sharing and governed data exchange rather than a dedicated “Kinaxis connector” authored by Databricks. That integration pattern is explicitly called out in the partnership press releases and supported by Databricks documentation.