The other area where you’re going to see drivers of workloads is that people get to search for data related to what their general angle of inquiry is in a much more effective manner than they have been able before. And this is also where it’s very important that you can search beyond enterprise boundaries, because the context of data is not limited by your enterprise boundaries. We can go on and on and on about our use cases, there is a million of them. As you get further down, you can start asking really, really hard questions that in prior periods, prior eras, we really needed to launch whole analyst teams to go research and investigate topics, where now the data will be able to — the systems will be able to generate queries and the type of data that will immediately, very, very quickly begin to generate insights.
And that’s by the way, that’s going to become the leading edge for structured proprietary data, which is, of course, the center of our universe.
Mike Scarpelli: And on your question on net revenue retention, I just want to remind you I’m not going to guide to net revenue retention. But I do think over time it is going to continue to converge closer to our growth rate. I do think it will stabilize, but I do expect it’s going to come down slightly from where it’s at right now just in what we’re seeing today.
Operator: Thank you. The next question will be from the line of Brad Zelnick with Deutsche Bank. Your line is now open.
Brad Zelnick: Great, thank you so much. I’ve got one for Frank and one for Mike. Frank, stable edges continue to tick up, which is great to see, and I know it’s an important metric for the company and its strategic vision. Anything else perhaps qualitative that you can share in terms of how data sharing is progressing? And for you Mike, great to see the margin upside, everybody is happy about it. But with such a huge opportunity, how can you be sure you’re striking the right balance of investment, especially when you’re up against such well-capitalized competitors? Thanks, guys.
Frank Slootman: On the topic of data sharing, we instrument that whole side of the business very, very carefully and we drive it on a quarterly basis. But sometimes data edges are very enterprise-specific. In other words, they just have things, the use cases, that just pertain to their business and these are bilateral relationships between Snowflake accounts and different institutions. But where it gets really interesting, where you get real network effect kicking in, is when you have industries or sub-industries where data sharing just makes sense. And obviously, in financial institutions, because financial institutions inherently have been pumping data around in massive, massive volumes for — literally for generations. This is an absolute no-brainer.
And we do the vast majority, historically data edges have been in the financial services sectors become almost a standard. This is how we move data from A to B to C. Asset management particularly has a really big need for that. But the other area, and again, this is an industry, in supply chain management. I mean, in the supply chain, there are multiple entities to the degree that they all have Snowflake accounts, it’s very easy to get visibility and supply chain across entities and being able to flag supply chain events much earlier and get visibility to that. So once you’re in a supply chain, there need to be on Snowflake and share data with your supply chain partners is going to become very, very compelling. And we announced at Summit and even earlier our relationship with Blue Yonder, for example, which is really the largest software company in the world in supply chain management, that they are re-platforming on Snowflake.
So, we think that’s another sort industry/sub-industry where every manufacturer, every retailer is going to become an opportunity for us. So it’s a little bit of color on how these things develop from our perspective.
