As cloud infrastructure projects grow increasingly complex, there’s been a trend in the industry to launch prepackaged solutions for specific verticals. Today, the well-funded data analytics firm Databricks is joining the fray with its first vertical-specific solution: Lakehouse for Retail. The promise here is to offer a fully integrated platform that can help retailers extract value from the vast volumes of data they generate, be that through traditional analytics or by leveraging Databricks’ AI tools.
“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,” said Databricks CEO and co-founder Ali Ghodsi. “Lakehouse for Retail will empower data-driven collaboration and sharing across businesses and partners in the retail industry.”
Some of the early adopters for the platform include the likes of Walgreens, Columbia and H&M Group. These users get access to the full Databricks platform, but also — and most importantly — a set of Lakehouse for Retail Solution Accelerators that offer what Databricks calls a “blueprint of data analytics and machine learning use cases and best practices,” which can ideally save new users months of development time. These include templates for real-time streaming data ingestion, demand forecasting, recommendation engines and tools for measuring customer lifetime value. It’s worth noting that Databricks previously offered similar blueprints, but customers had to assemble these for themselves instead of Databricks offering them as part of an integrated solution.
“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, vice president, Pharmacy and HealthCare Platform Technology 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.”
Over the course of the last few years, Databricks has been trying to popularize the concept of the “lakehouse,” which combines the best of data warehouses for analytics and data lakes for storing vast amounts of raw data that has yet to be operationalized.
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