Customer Analytics At Scale With Segment
Show notes
Summary Customer analytics is a problem domain that has given rise to its own industry. In order to gain a full understanding of what your users are doing and how best to serve them you may need to send data to multiple services, each with their own tracking code or APIs. To simplify this process and allow your non-engineering employees to gain access to the information they need to do their jobs Segment provides a single interface for capturing data and routing it to all of the places that you need it. In this interview Segment CTO and co-founder Calvin French-Owen explains how the company got started, how it manages to multiplex data streams from multiple sources to multiple destinations, and how it can simplify your work of gaining visibility into how your customers are engaging with your business. 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We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Your host is Tobias Macey and today I’m interviewing Calvin French-Owen about the data platform that Segment has built to handle multiplexing continuous streams of data from multiple sources to multiple destinations Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Segment is and how the business got started? What are some of the primary ways that your customers are using the Segment platform? How have the capabilities and use cases of the Segment platform changed since it was first launched? Layered on top of the data integration platform you have added the concepts of Protocols and Personas. Can you explain how each of those products fit into the overall structure of Segment and the driving force behind their design and use? What are some of the best practices for structuring custom events in a way that they can be easily integrated with downstream platforms? How do you manage changes or errors in the events generated by the various sources that you support? How is the Segment platform architected and how has that architecture evolved over the past few years? What are some of the unique challenges that you face as a result of being a many-to-many event routing platform? In addition to the various services that you integrate with for data delivery, you also support populating of data warehouses. What is involved in establishing and maintaining the schema and transformations for a customer? What have been some of the most interesting, unexpected, and/or challenging lessons that you have learned while building and growing the technical and business aspects of Segment? What are some of the features and improvements, both technical and business, that you have planned for the future? Contact Info LinkedIn @calvinfo on Twitter Website calvinfo on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Segment AWS ClassMetric Y Combinator Amplitude web and mobile analytics Mixpanel Kiss Metrics Hacker News Segment Connections User Analytics SalesForce Redshift BigQuery Kinesis Google Cloud PubSub Segment Protocols data governance product Segment Personas Heap Analytics Podcast Episode Hotel Tonight Golang Kafka GDPR RocksDB Dead Letter Queue Segment Centrifuge Webhook Google Analytics Intercom Stripe GRPC DynamoDB FoundationDB Parquet Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast
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