Braze teams up with Databricks on data integration

Braze has announced a strategic partnership and bi-directional integration with Databricks, linking customer engagement tools with Databricks' data platform.

The integration is designed to let brands move customer data and audience information directly between the two systems without separate middleware. It combines Databricks CustomerLake for audience management, Databricks Model Serving for AI-driven workflows, and Delta Sharing to return engagement data to the Databricks environment.

The move reflects growing demand from marketers and data teams to reduce the burden of stitching together customer data systems, engagement platforms, and analytics tools. Vendors have increasingly focused on tighter links between data warehouses, lakehouses, and marketing applications as companies look to cut software sprawl and speed up the use of customer signals.

Braze customers already use its Cloud Data Ingestion product to synchronise user data from Databricks. Under the broader tie-up, Databricks CustomerLake users will also be able to send governed cohort audiences into Braze for segmentation and campaign activity.

Data flow

At the centre of the partnership is an effort to keep customer data inside a company's core data estate while still making it available to marketing teams. The setup is described as a zero-copy approach, meaning sensitive customer profiles can remain under the brand's control rather than being duplicated across multiple systems.

That matters for businesses trying to balance personalisation with tighter data governance rules and internal oversight. It also addresses a common complaint from large organisations that custom data pipelines are costly to build and difficult to maintain over time.

Ed McDonnell, Chief Revenue Officer at Braze, said the link was intended to reduce friction between data analysis and customer communications.

"CustomerLake represents a real shift in how brands can act on their data, and Braze is proud to be at the centre of it. By embedding directly into Databricks, we're giving customers the ability to move from insight to engagement without the friction, latency, or cost of middleware. The brands that win today are the ones that can meet their customers in the moment. That's exactly what this makes possible," said Ed McDonnell, Chief Revenue Officer at Braze.

Braze said the architecture removes the need for standalone customer data platforms or reverse ETL products in some use cases. Reducing those layers could also lower total cost of ownership and ease pressure on engineering teams maintaining fragile scripts and data transfers.

AI workflows

The partnership also extends to Braze's AI tools. Braze recently introduced its Agent Console, which allows marketers to create and deploy automated agents within customer journeys that can generate content, make decisions, and enrich data.

Those agents rely on large language models. Customers can use a model provided through Braze or connect external providers such as OpenAI, Anthropic, or Google Gemini. With the Databricks integration, they will also be able to use Databricks Model Serving, which includes proprietary and open-source models hosted by Databricks.

The addition gives companies another way to manage AI model usage within their existing Databricks environment. For businesses concerned about governance, spend, and oversight of generative AI tools, centralising that activity in the same platform as their customer data may be attractive.

Feedback loop

Another element of the announcement is the return flow of campaign data back into Databricks through Delta Sharing. Engagement data such as messages sent, opened, clicked, and bounced can be streamed back into Databricks continuously.

That feedback loop is intended to help marketers respond to customer activity more quickly, including in areas such as abandoned basket messages, price alerts, and travel notifications. It also gives data teams a way to combine live campaign information with transactional records held in the lakehouse, supporting financial attribution analysis.

The deal also drew support from Stitch, a services partner that works with enterprise clients across sectors including retail, streaming, and healthcare.

"The biggest bottleneck in enterprise marketing is getting the right data. The new Braze Data Platform enhancements, including the bidirectional Databricks integration, fundamentally change that by solving the data handoff problem at the platform level. Marketers can now act on signals the same day they exist, not the same quarter. Stitch has been doing this work with enterprise brands across retail, streaming, and healthcare, and the limiting factor has consistently been the data handoff problem. That problem is now solved at the platform level. We're proud to be the only services partner named in both the Braze and Databricks CustomerLake launches because we've seen firsthand what this unlocks for our clients," said Michael Burton, Chief Executive Officer at Stitch.