George Fraser

Yeah, well, first of all, there's several layers to that the first one you is actually the testing that we do on our end to validate that all of our sink strategies, all those little details I mentioned a minute ago are actually working correctly, our testing problem is quite difficult, because we interoperate with so many external systems. And in many cases, you really have to run the tests against the real system for the test to be meaningful. And so our build architecture is actually one of the more complex parts of five train, we use a build tool called Bazell. And we've done a lot of work, for example, to run all of the databases and FTP servers and things like that, that we have to interact with in Docker containers so that we can actually produce reproducible Ed tests. So that actually is one of the more complex engineering problems at five trend. And if that sounds interesting to you, I encourage you to apply to our engineering team, because we have lots more work to do on that. So that's the first layer is really all of those tests that we run to verify that our sync strategies are correct. The second layer is that, you know, is it working in production is the customers data actually getting sick and as a getting synced correctly, and one of the things we do there that may be a little unexpected to people who are accustomed to building data pipelines themselves is all five trans data pipelines are typically fail fast. That means if anything unexpected happens, if we see, you know, some event from an API endpoint that we don't recognize, we stop. Now, that's different than when you build data pipelines yourself, when you build data pipelines for your own company, usually, you will have them try to keep going no matter what. But five train is a fully managed service. And we're monitoring and all the time. So we tend to make the opposite choice of anything suspicious is going on, the correct thing to do is just stop and alert five Tran, hey, go check out this customers data pipeline, what the heck is going on? Something unexpected happen is happening. And we should make sure that our sync strategies are actually correct. And then that brings us to the last layer of this, which is alerting. So when data pipelines fail, we get alerted and the customer gets alerted at the same time. And then we communicate with the customer. And we say hey, we may need to go in and check something Do I have permission to go, you know, look at what's going on in your data pipeline in order to figure out what's going wrong, because five trained as a fully managed service. And that is critical to making it work. When you do we do and you say we are going to take responsibility for actually creating an accurate replica of all of your systems in your data warehouse. That means you're signing on to comprehend and fix every little detail of every data source that you support. And a lot of those little details only come up in production when some customer shows up. And they're using a feature of Salesforce that Salesforce hasn't sold for five years, but they've still got it. And you've never seen it before. Some of a lot of those little things only come up in production. The nice thing is that that set of little things, well, it is very large, it is finite. And we only have to discover each problem once and then every customer thereafter. benefits from that. Thanks