Swiggy is using Snowflake to bring data from its food delivery, Instamart, and Dineout businesses into a central analytical layer. The company said the setup has improved the performance of Swiggy’s slowest data workflows by 90% to 96%, while some heavy queries now take 15 minutes instead of two hours.
The move addresses a challenge that emerged as Swiggy expanded across multiple businesses. Data was spread across different systems, making it harder for teams to access insights quickly. Swiggy wanted a common data layer that could handle peak workloads while allowing employees to access information without relying on a central data team for every request.
Data processing moves closer to real time
Swiggy established a central analytical layer on Snowflake using Apache Iceberg. According to Snowflake, data processing that previously took about six hours can now be completed close to real time.
The company also said critical business reports are being delivered reliably on schedule. The improvement is intended to support faster decisions across marketing, product engineering, operations, and finance.
Marketing teams can build and launch campaigns using their own tools. Product engineering teams use standardised metric definitions to assess experimental features before release through an in-house platform running on Snowflake.
Operational teams can monitor service quality metrics in real time, while finance teams can see technology expenses at the workload level.
Self-service access for Swiggy teams
The data setup also gives employees across Swiggy’s businesses self-service access to information. The goal is to reduce dependence on central data teams when employees need to understand customer and partner needs.
Security controls remain part of this access model. Swiggy uses role-based access, column masking, and row-level security so internal teams and external partners can work with protected data.
The company said Snowflake’s governance framework also applies these controls to artificial intelligence (AI) agents and applications. AI systems can operate within the same permission boundaries, use short-lived credentials, and maintain audit trails required for human users.
AI becomes part of the data foundation
Swiggy’s data infrastructure is also being positioned to support AI-based applications. The company said its approach applies the same governance principles to people and AI agents.
Madhusudhan Rao, chief technology officer at Swiggy, said the value of data depends on whether it reaches the person who can act on it.
“At Swiggy, data is valuable only when it reaches the person who can act on it, whether that is a city sales manager, restaurant owner or delivery partner. With Snowflake, we are making trusted, governed insights easier to access while ensuring that every user and AI agent operates within the same permissions and audit framework,” Rao said.
Vijayant Rai, managing director for India at Snowflake, said Swiggy’s centralised data foundation is intended to reduce the need for every data question to pass through a central team.
The companies announced the development on September 10, 2026, at a time when Swiggy is handling data across food delivery, quick commerce, and dining out operations.