Databricks announced the Databricks Lakehouse for Retail, the company’s first industry-specific data lakehouse for retailers and consumer goods (CG) customers. With Databricks’ Lakehouse for Retail, data teams are enabled with a centralized data and AI platform that is tailored to help solve data challenges that retailers, partners, and their suppliers are facing.
Databricks’ Lakehouse for Retail delivers an open, flexible data platform, data collaboration and sharing, and a collection of tools and partners. Designed to jumpstart the analytics process, new Lakehouse for Retail Solution Accelerators offer a blueprint of data analytics and machine learning use cases and practices to save development time for data engineers and data scientists. Solution accelerators include:
- Real-time Streaming Data Ingestion: with point-of-sale, mobile application, inventory and fulfillment data.
- Demand forecasting and time-series forecasting: with fine-grained demand forecasting to predict demand for items and stores.
- ML-powered recommendation engines: specific recommendations models including neural network, collaborative filtering, content-based recommendations.
- Customer Lifetime Value: predict behaviors of churn, and segment consumers with customer analytics accelerators to help improve decisions on product development and promotions.
Databricks partners like Deloitte and Tredence are delivering pre-built analytics solutions on the lakehouse platform that address real-time customer use cases.