Saurav is a seasoned engineer with over 9 years of experience as Software engineer, solution architect and product manager. Worked on Visa Inc’s first product foray into big data, building next-generation data processing platforms, leveraging Spark and related tech stack. Currently solving problems of scale in Zalando as data engineer with focus on spark and databricks platform adoption. Patent filed(pending state) for recommendations based on predicted behaviour. Holds master’s degree in Software engineering from National University of Singapore.
October 16, 2019 05:00 PM PT
Zalando SE is Europe's leading online fashion platform and connects customers, brands and partners. With millions of visitors each month, we have petabytes of purchase, click-stream, product and other data in our data lake. This data is crucial to powering insights on shopper behavior and driving an AI-first strategy to improve site engagement.
Over 7 months ago, Zalando adopted Apache Spark, Delta Lake and Databricks as its de-facto computation platform for analytics and machine learning. During this period, we onboarded well over 50 internal teams ranging from BI teams, with no knowledge of Spark or big data running ETL pipelines to AI/ML teams already using EMR and Spark for heavy model training. Provided the spectrum of varied business problems they were trying to solve, we worked with each team individually, understanding their use cases, helping them validate assumptions, developing working code and taking them to production. In this talk we will share best practices for building a unified data and analytics architecture on Databricks, lessons learned rolling it out across the organization and provide a deep dive on AI & Analytics use cases in the fashion ecommerce space.