Highlights:

  • The newly announced integration will allow users to access data stored in the Databricks Lakehouse Platform directly from the Salesforce Data Cloud interface and vice versa.
  • Through the fresh integration between the two platforms, an organization can transfer records from Salesforce to its Databricks environment and utilize these records for training its AI models.

Salesforce Inc. and Databricks Inc. have introduced a new product integration to streamline data analytics projects for their joint customers.

This integration was created through a technical partnership that the companies disclosed recently. This news follows Salesforce’s recent partnership with Snowflake Inc., a major competitor of Databricks. It precedes Salesforce’s annual Dreamforce conference in San Francisco.

The latest technical collaboration announced centers around two products: the Databricks Lakehouse Platform and the Salesforce Data Cloud.

The flagship product of Databricks is the Databricks Lakehouse Platform. It’s a data lakehouse, a software solution that empowers businesses to store extensive data, extract valuable insights through analysis, and train machine learning models. A lakehouse amalgamates the functionalities of two previous product classifications: data warehouses and data lakes.

The Salesforce Data Cloud is another area of concentration in the companies’ collaboration. This cloud-based platform empowers enterprises to centralize customer data storage in one location. This data can be leveraged to enhance sales teams’ product recommendations, create personalized advertisements, and support various go-to-market initiatives.

The newly announced integration will allow users to access data stored in the Databricks Lakehouse Platform directly from the Salesforce Data Cloud interface and vice versa. The companies highlight that this integration is well-suited for various analytics use cases.

For instance, a retailer could merge its customer data stored in Salesforce with information about competitors’ advertising campaigns from its Databricks lakehouse. Subsequently, the retailer could correlate these two datasets to analyze how alterations in competitors’ marketing strategies impact customer demand.

Additionally, it’s feasible to synchronize records in the opposite direction, transferring data from Salesforce to the Databricks Lakehouse Platform. The latter software encompasses a toolkit for constructing artificial intelligence models. Through the fresh integration between the two platforms, an organization can transfer records from Salesforce to its Databricks environment and utilize these records for training its AI models.

Adam Conway, the Senior Vice President of Products at Databricks, said, “Access to trusted, governed data is critical for every company, and the ability to combine that data with AI is now essential to remain competitive. Delivering best-in-class integrations with Salesforce builds on our longtime partnership and will enable more organizations to securely share reliable, high-quality data and AI models across platforms.”

Recently, Salesforce unveiled an integration with Snowflake’s renowned cloud data platform, enabling organizations to merge their Salesforce data with information from their Snowflake environments. In terms of the Databricks integration, there is no requirement for third-party ETL tools to facilitate the transfer of data between the platforms.