Digging Into Data Replication At Fivetran

Aug 12, 2019

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Summary

The extract and load pattern of data replication is the most commonly needed process in data engineering workflows. Because of the myriad sources and destinations that are available, it is also among the most difficult tasks that we encounter. Fivetran is a platform that does the hard work for you and replicates information from your source systems into whichever data warehouse you use. In this episode CEO and co-founder George Fraser explains how it is built, how it got started, and the challenges that creep in at the edges when dealing with so many disparate systems that need to be made to work together. This is a great conversation to listen to for a better understanding of the challenges inherent in synchronizing your data.

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Your host is Tobias Macey and today I’m interviewing George Fraser about FiveTran, a hosted platform for replicating your data from source to destination

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing the problem that Fivetran solves and the story of how it got started?
Integration of multiple data sources (e.g. entity resolution)
How is Fivetran architected and how has the overall system design changed since you first began working on it?
monitoring and alerting
Automated schema normalization. How does it work for customized data sources?
Managing schema drift while avoiding data loss
Change data capture
What have you found to be the most complex or challenging data sources to work with reliably?
Workflow for users getting started with Fivetran
When is Fivetran the wrong choice for collecting and analyzing your data?
What have you found to be the most challenging aspects of working in the space of data integrations?}}
What have been the most interesting/unexpected/useful lessons that you have learned while building and growing Fivetran?
What do you have planned for the future of Fivetran?

Contact Info

LinkedIn
@frasergeorgew on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?