Recently, Deichmann published a customer story describing how Lakebase enabled seamless omnichannel marketing. This blog covers the technical side of the story.

Every retail company needs to leverage data to deliver personalized, high-performance marketing campaigns. Nevertheless, we see some inefficiencies across the industry:

A lakebase is a new, open architecture that combines the best elements of transactional databases with the flexibility and economics of the data lake. Databricks Lakebase Postgres, our implementation of the lakebase architecture, solves these problems:

To illustrate the benefits of using Lakebase as the backend database for our marketing campaign platform, we will show how to integrate Lakebase with SAP Engagement Cloud, an omnichannel marketing platform, and launch a personalized marketing campaign based on customer segments previously created in the Lakehouse.

We set up our Postgres instance by creating a new Lakebase Autoscaling project. A project is the top-level container for our database resources. A newly created project includes a production database, which will be the PostgreSQL instance that SAP Engagement Cloud connects to.

Marketing campaigns rely on time-based triggers. When a campaign is triggered, SAP Engagement Cloud queries the database to retrieve prospects that meet the specified criteria. These mechanics induce periodic spikes within extended lows. For this reason, for compute, we scale to 0 for the extended lows, eliminating compute costs for these periods, and set a medium capacity of 16 CU (~32 GB RAM) as the maximum for the spikes. Even if the chosen memory range is relatively large, Lakebase autoscaling speed and reactivity eliminate the risk of resource underutilization, which lowers TCO and reduces the need for sizing and provisioning our database.