Posttransaction processing using Apache Pulsar at Narvar
Who is this presentation for?
- Data engineers, managers, and data platform engineers
Narvar enables leading retailers to deliver postpurchase experiences that retain, engage, and delight their customers from cart to doorstep and beyond. The Narvar platform allows retailers to engage and communicate with customers, driving customer satisfaction and long-term customer loyalty.
Narvar originally used a large collection of point technologies such as AWS Kinesis, Lambda, and Apache Kafka to satisfy its requirements for pub/sub messaging, message queuing, logging, and processing. After evaluating various technology options to help simplify and consolidate its data processing, Narvar selected Apache Pulsar for its unified messaging and queuing model, ability to process data using lightweight functions, and guaranteed data durability.
Karthik Ramasamy and Anand Madhavan provide an overview of the Narvar use-case patterns and how it implemented those patterns using Apache Pulsar.
- A basic knowledge of data processing (useful but not required)
What you'll learn
- Discover how retail use cases are elegantly mapped into Apache Pulsar and the production experiences
Anand Madhavan is the vice president of engineering at Narvar. Previously, he was head of engineering for the Discover product at Snapchat and director of engineering at Twitter, where we worked on building out the ad serving system for Twitter Ads. He earned an MS in computer science from Stanford University.
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