Swiggy adopts Snowflake as unified data foundation to accelerate insights
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Snowflake has announced that Swiggy is leveraging its platform as a unified data foundation to support marketing, operations and business execution across its food delivery, Instamart and Dineout businesses. As Swiggy scaled to serve millions of customers across its businesses, fragmented data created challenges in accessing insights. The company sought a unified serving layer capable of supporting peak workloads, while also enabling teams to access insights through self-service tools and reducing dependency on central data teams. Swiggy has established a central analytical layer on Snowflake using Apache Iceberg. According to Snowflake, this has improved the company's slowest data workflows by 90% to 96%. The company has also reduced the processing time for its heaviest queries from two hours to 15 minutes, while data processing has moved from six hours to near real time. The improvements are being used across functions. Marketing teams can build and launch targeted campaigns directly within 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 are using real-time service quality metrics to monitor and proactively improve delivery logistics. Finance teams, meanwhile, have gained workload-level visibility into technology expenses...
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