Databricks Introduces Data Lakehouse for Retail

The new platform enables data teams to solve critical retail challenges

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Databricks Introduces Data Lakehouse for Retail
Data & AnalyticsLatest News

Published: January 20, 2022

Sandra Radlovački

Sandra Radlovački

Databricks has announced its first industry-specific data lakehouse for retail.

The new solution enables teams to work with a centralised data and AI platform to solve the critical data challenges that retailers, partners, and their suppliers face.

Ali Ghodsi, CEO and Co-Founder at Databricks, said: “Databricks has always innovated on behalf of our customers and the vision of lakehouse helps solve many of the challenges retail organizations have told us they’re facing.”

“This is an important milestone on our journey to help organizations operate in real-time, deliver more accurate analysis, and leverage all of their customer data to uncover valuable insights. Lakehouse for Retail will empower data-driven collaboration and sharing across businesses and partners in the retail industry.”

Lakehouse for Retail delivers an open and flexible data platform alongside data collaboration and sharing. Data teams can leverage a collection of powerful tools for data analytics and machine learning to save time spent on development.

Within Databricks’ Lakehouse for Retail, customers can use solution ‘accelerators’, to enhance the effect of the platform. These accelerators include real-time streaming data ingestion, demand forecasting and time-series forecasting, and ML-powered recommendation engines.

Luigi Guadagno, Vice President, Pharmacy and HealthCare Platform Technology at Walgreens, said the new Lakehouse allowed the company to “unify all of its data and store it in one place, by eliminating complex and costly legacy data silos”. Guadagno also said the cross-domain collaboration gave the company the necessary flexibility to adapt and better serve customers.

Companies that have already adopted Databricks’ new Lakehouse include the likes of Columbia, H&M, Reckitt, Acosta, and many others.

 

 

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