Retail data is expanding at an unprecedented rate, demanding a scalable, cost-efficient, and near real-time architecture. At Unilever, we transformed our data management approach by leveraging Databricks Lakeflow Declarative Pipelines, achieving approximately $500K in cost savings while accelerating computation speeds by 200–500%.By adopting a streaming-driven architecture, we built a system where data flows continuously across processing layers, enabling real-time updates with minimal latency.Lakeflow Declarative Pipelines' serverless simplicity replaced complex-dependency management, reducing maintenance overhead, and improving pipeline reliability. Lakeflow Declarative Pipelines Direct Publishing further enhanced data segmentation, concurrency, and governance, ensuring efficient and scalable data operations while simplifying workflows.This transformation empowers Unilever to manage data with greater efficiency, scalability, and reduced costs, creating a future-ready infrastructure that evolves with the needs of our retail partners and customers.
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Speaker
Evan Cherney
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talks
Senior Data Science Manager
Unilever
With nearly a decade of experience in the Data and AI realm, Evan Cherney brings deep expertise across the manufacturing, telecommunications, and consumer packaged goods (CPG) industries. His career spans roles as a Data Scientist, Data Engineer, and Data Architect—delivering scalable, AI-powered solutions that drive business impact. Holding a bachelor's degree in Finance and a master's in Data Science, he blends technical depth with business acumen to modernize data infrastructure and accelerate innovation across complex enterprise environments.
Bio from: Data + AI Summit 2025
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