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- 1Databricks Shows How to Build Real-Time Retail RecommendationsโผReal-Time Retail Intelligence: Building E-Commerce Recommendations with Lakebase and AI Search on Databricks
Databricks has published a walkthrough of building real-time e-commerce recommendation systems, combining its Lakebase database service with AI Search on the Databricks platform. The approach targets retailers looking to deliver personalised product suggestions to shoppers instantly, using live data rather than batch processing. The piece is aimed at data and engineering teams in retail evaluating tools for customer personalisation.
- 2Databricks outlines real-time retail recommendation architectureโ๐ Real-Time Retail Intelligence: Building E-Commerce Recommendations with Lakebase and AI Search on Databricks The oppor
Databricks has published guidance on building real-time e-commerce recommendation systems using its Lakebase database and AI Search capabilities. The company frames personalization as a revenue engine for retailers, arguing that every second of shopper engagement can be converted into tailored product suggestions. The piece walks through how unified data platforms can power retail intelligence at scale.
- 3Databricks launches branch-based restores for Lakebase Postgresโ๐ Lakebase Postgres branch-based restores for fast recovery at scale In managed OLTP, restores have always been painfull
Databricks has announced branch-based restore capability for Lakebase Postgres, its managed OLTP database service. Restores in managed transactional databases have historically been slow, and get slower as data volumes grow. The new feature lets users recover data quickly at scale by using branching, a technique more common in development workflows, to restore only what is needed rather than entire databases.