• Blazing Fast OLAP on Uber’s Inventory and Catalog Data with Apache Pinot™ 9 December 2025 / GlobalIntroduction You can order almost anything with Uber Eats. • Whether you want to order an iced vanilla latte from your local cafe or order a new coffee machine to kickstart your own coffee-making journey, Uber Eats’ massive catalog of stores and items has got you covered. • From an engineering perspective, managing such a massive collection of items poses really interesting challenges. • Outside the user-facing serving layer, you also need to support internal tools that allow operations teams and engineers to manage catalogs and items in real time and with low-latency. • Users not only need text search functionality to look up specific items; they also want fast analytics to get broad insights quickly and seamlessly. • This blog shares how we adopted and scaled Apache Pinot™ to handle the massive volume of Uber’s catalog data to power multiple internal tools and workflows.
Article Summaries:
- Uber has deployed Apache Pinot to power real‑time analytics and search on its expansive Uber Eats catalog. The new INCA (Inventory and Catalog) system models products and store‑specific items in a dynamic hierarchy, where a single change can cascade across thousands of records. Existing storage in a Docstore and batch‑oriented Hive were insufficient for the low‑latency, high‑volume demands of catalog management. Pinot now indexes billions of rows and handles hundreds of thousands of updates per second, enabling internal tools to perform fast filtering, searching, and aggregations within minutes rather than hours. This shift delivers near‑real‑time visibility and consistency across Uber’s catalog workflows.
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