Swiggy adopts Snowflake to speed up data workflows

Swiggy has adopted Snowflake as its unified data foundation across its food delivery, quick commerce and dining-out businesses.

The new setup improved Swiggy's slowest data workflows by 90 to 96 per cent, cut its heaviest queries from two hours to 15 minutes, and reduced data processing times from six hours to near real time.

Swiggy has been expanding across multiple consumer services in India, including food delivery, Instamart and Dineout. That growth increased the volume of operational and customer data moving across the business and made it harder for teams to work from a single analytical view.

It needed a common serving layer that could support peak workloads and give more staff direct access to information without relying on a central data team. Swiggy built that analytical layer on Snowflake using Apache Iceberg.

Operational use

The system is designed to support decision-making across marketing, product, operations and finance. Marketing teams can build and launch targeted campaigns in their own tools, while product engineering teams use standardised metric definitions to assess experimental features before release through an in-house platform running on Snowflake.

Operational teams use the platform to track service quality measures in real time, helping improve delivery logistics. Finance teams also gain visibility into technology costs at the workload level.

The arrangement also changes how data is accessed across the organisation. Employees in different business lines now have self-service access to information, with controls in place to limit what they can see and use.

Security measures include role-based access, column masking and row-level security. Those controls allow internal teams and external partners to work with masked data while maintaining oversight.

AI governance

Another focus is preparing the data foundation for AI tools and automated systems. Snowflake said its governance framework applies the same permission boundaries, short-lived credentials and audit trails to AI-driven agents and applications as it does to human users.

That matters for companies such as Swiggy as they look to deploy more automated decision tools while managing the risk of access to sensitive operational and customer information. In practice, AI systems are expected to operate under the same rules as employees.

Madhusudhan Rao outlined how Swiggy sees the role of wider access to information across its operations.

"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," said Madhusudhan Rao, Chief Technology Officer, Swiggy.

For Snowflake, the Swiggy deployment adds to its work with large consumer-facing businesses that need to process and analyse high volumes of data across multiple services. Snowflake framed the project as a shift away from routing every request through a specialist internal team.

"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," said Rai.