Swiggy has selected Snowflake as its unified data foundation to support data-driven decision-making across its food delivery, quick-commerce and dining-out businesses.
Announced by Snowflake on September 10, the partnership comes as Swiggy scales across multiple businesses, creating increasingly complex data requirements. The company has been using Snowflake to bring fragmented data together, improve the performance of critical workloads and give teams across the organisation self-service access to business insights.
According to Snowflake, Swiggy has improved its slowest data workflows by 90% to 96%, while its heaviest queries have been reduced from around two hours to 15 minutes. Data processing that previously took up to six hours can now support near real-time decision-making.
As Swiggy expanded across food delivery, Instamart and Dineout, the company faced the challenge of managing and analysing data generated across different businesses.
To address this, Swiggy established a central analytical layer on Snowflake using Apache Iceberg, creating a unified serving layer designed to handle workloads at scale, including during periods of high demand.
The setup is also intended to reduce Swiggy’s dependence on central data teams by giving employees across different functions access to governed, self-service insights.
For marketing teams, this means they can build and launch targeted campaigns directly through their own tools. Product engineering teams can use standardised metrics to assess experimental features before they are released, while operational teams can monitor service-quality metrics in real time to identify and address issues in delivery operations.
Finance teams, meanwhile, can gain visibility into technology expenses at the workload level.
The company is also using Snowflake’s governance capabilities to control how data is accessed internally and by external partners.
Features including role-based access, column masking and row-level security allow teams and partners to work with data while protecting sensitive information.
The same governance framework extends to Swiggy’s AI applications and agents. According to Snowflake, AI-powered agents operate within the same permission boundaries, short-lived credential requirements and audit trails that apply to human users.
Madhusudhan Rao, Chief Technology Officer- Swiggy, said, “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,”
Vijayant Rai, Managing Director- India, Snowflake, said, “India’s fastest-moving companies can’t afford to route every data question through a central team. With Snowflake, Swiggy has built a centralised, governed data foundation, consistent controls, and an AI framework that holds agents to the same standards as people. This approach is helping Swiggy create a path toward more capable and accountable AI agents,”
