Posttransaction processing using Apache Pulsar at Narvar
Who is this presentation for?Data Engineers, Managers, Data Platform Engineers
Narvar enables leading retailers to deliver post-purchase experiences that retain, engage, and delight their customers from cart to doorstep and beyond. The Narvar platform enables 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 their requirements for pub/sub messaging, message queuing, logging and processing. After evaluating various technology options to help them simplify and consolidate their data processing, Narvar selected Apache Pulsar due to its unified messaging and queuing model, ability to process data using lightweight functions, and guaranteed data durability.
In this talk, we will provide an overview of the Narvar use case patterns and how Narvar implemented those patterns using Apache Pulsar.
Prerequisite knowledgeBasic knowledge of data processing will be helpful but not necessary.
What you'll learn
Karthik Ramasamy is the cofounder of Streamlio, a company building next-generation real-time processing engines. Karthik has more than two decades of experience working in parallel databases, big data infrastructure, and networking. Previously, he was engineering manager and technical lead for real-time analytics at Twitter, where he was the cocreator of Heron; cofounded Locomatix, a company that specialized in real-time stream processing on Hadoop and Cassandra using SQL (acquired by Twitter); briefly worked on parallel query scheduling at Greenplum (acquired by EMC for more than $300M); and designed and delivered platforms, protocols, databases, and high-availability solutions for network routers at Juniper Networks. He is the author of several patents, publications, and one best-selling book, Network Routing: Algorithms, Protocols, and Architectures. Karthik holds a PhD in computer science from the University of Wisconsin-Madison with a focus on databases, where he worked extensively in parallel database systems, query processing, scale-out technologies, storage engines, and online analytical systems. Several of these research projects were spun out as a company later acquired by Teradata.
Anand Madhavan is VP of Engineering at Narvar. Before that he was the head of engineering for the Discover product at Snapchat and prior to that was the Director of Engineering at Twitter where we worked on building out the ad serving system for Twitter Ads. He has an MS in computer science from Stanford University.
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