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How Instacart Met Real-Time Grocery Needs with Data Streaming
Confluent has really been an accelerant to the work that my team does.
Challenge
Demand became infinite for Instacart’s grocery delivery service nearly overnight when the pandemic shut down much of public life in 2020. The company gained half a million new customers in mere weeks–and needed to serve each of them with real-time availability.
Solution
Instacart deployed Confluent Cloud to build new systems and keep food flowing in the U.S., supporting 10 years of growth in six weeks across 59,000 locations. They’ve created a scalable data streaming platform so customers can see inventory availability and get what they want, when they want it.
Results
- Extreme scalability to quickly serve hundreds of thousands of new customers
- Fraud detection and better inventory availability, with more emerging use cases
- Establishment of data backbone to unite teams across company
- Reduced TCO vs. managing open-source Kafka
Instacart started with a single streaming use case, then moved quickly to mission-critical apps and a data-centric infrastructure.
Instacart’s initial success with data streaming informed their larger strategy–building a central data mesh so teams can get what they need.
Few companies grow as fast as Instacart did in 2020. When the Covid pandemic shut down much of public life in March 2020, Instacart became the essential way to get groceries. The data infrastructure team had to onboard 500,000 new customers in a matter of weeks. The real-time necessity meant the Confluent team had to evolve systems in real time, considering each day how they could increase capacity.
Instacart adopted Confluent to dramatically scale its data systems. The team was familiar with Apache Kafka and knew it could help run its massive inventory system. Confluent Cloud helped them immediately spin up data streaming to create real-time availability models, fraud detection systems, and more, without having to hire a team to manage Kafka.
More Customer Stories
Kafka helps us produce contextually rich data for both IT and our business units. Kafka also enables us to deploy more advanced techniques in-stream, such as machine learning models that analyze data and produce new insights
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