Databricks targets retail vertical with its first industry-specific lakehouse Posted on : Jan 14 - 2022

San Francisco-based Databricks, a company that offers the capabilities of a data warehouse and data lake in a single “lakehouse” architecture, today announced its first industry-specific offering: Lakehouse for Retail.

Designed for enterprises dealing in the retail and consumer goods vertical, Databricks says Lakehouse for Retail is a fully integrated platform that aims to solve the most critical challenges retailers and their partners face while trying to leverage surging data volumes for AI and analytics projects.

The solution, which is generally available as of today, has already seen early adoption from major retail enterprises including Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51°, Co-Op Food, Gousto, and Acosta.

“With hundreds of millions of prescriptions processed by Walgreens each year, Databricks’ Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads,” said Luigi Guadagno, the VP of pharmacy and healthcare platform at Walgreens.

“By eliminating complex and costly legacy data silos, we’ve enabled cross-domain collaboration with an intelligent, unified data platform that gives us the flexibility to adapt, scale and better serve our customers and patients,” Guadagno said.

Lakehouse for Retail: What’s special?

Rob Saker, the retail and manufacturing lead at Databricks, said the new retail lakehouse is based on open source and open standards, which allows retailers to share data — such as inventory levels, consumer data, sales data — with their partners/suppliers and collaborate with them on white label joint analytics, even if they are on a different cloud platform.

The offering also includes a suite of free solution accelerators that offer a blueprint of data analytics and machine learning use cases, as well as best practices to help enterprises get started and prototype AI projects in days and weeks. This, Databricks says, would cover multiple aspects, starting from streaming data ingestion for real-time decision-making (a must for winning omnichannel retail), demand and time-series forecasting, ML-powered recommendation engines for streamlining buyer journey, customer lifetime value analytics. View more