Matt Cain: We think it’s quite significant. And as we look into the future, we start in thinking about what are trying to solve with their application strategy. And every company around the world is trying to figure out how to use technology to improve their business and get closer to their customers. And in order to do that, you need to not only have all of the operational data at your disposal, but to make these applications truly real time and adaptive taking their capabilities to the next level is injecting data that you’re analyzing alongside the operational data store. And as much as people are focused on analytics in the world, the vast majority of enterprise data is not analyzed. And furthermore you have this significant gap, this latency gap that’s existed for 50 years in the wall that exists between operational and analytical data.
What we’ve done with this capability and our underlying architecture is enable a developer to develop these real time adaptive applications by combining not just the data that we manage on our data store, but ingesting data from S3, from other relational sources, sources like Mongo in a real time basis and then feed those insights directly into the application while it’s performing. There’s not eliminating ETL, reducing efficiency and with the underlying architecture to maintain the operational performance in SLA that modern databases require. So this is quite a big step for us. And when we think about the data layer for applications in an AI driven world obviously we’ve talked about vector being on the roadmap which will add to our architecture.
But we like to think about the density of the application that we are supporting in that data layer. Operational, analytical, and all of these capabilities coming together in a single platform is our advantage and we think that never have our core capabilities and the platform that we’ve developed with these scale and performance cloud to edge requirements ever been more relevant. And so we’re quite excited about that and getting quite frankly pretty phenomenal feedback from customers that understand the importance of this and aligning it to their overall digital transformation and application strategies.
Jason Ader: And can you give us some examples of use cases for the columnar analytics technology? Please proceed with your question.
Matt Cain: Yes. So, this is about like taking applications to the next level. So I mean I’ll give you a few generic ones. Let’s say you’ll find something on an e-commerce store and we all know you get a recommended product. But what if that recommendation was completely personalized to you? What if the product descriptions matched what was happening in your life? If you’re shopping in a home improvement store and it has the awareness that you’re doing a home remodel, well, now the product descriptions map the type of home remodel that you’re delivering and then can bring in the inventory that meets those needs at your local store. These are real time personalized occasions that are changing based on what’s happening in your world with you as a user.
Streaming services often recommend shows for us, but what if it’s aware of what’s happening in my connected home and recommends to pause something because the beer in my fridge is now the temperature that I like it and I’m not going to miss a particular portion of that game. Or I get an alert that another one of my favorite teams is starting that that level of adaptive application without slowing down the performance taking it to the next level. That’s what happens when you take all of these analytics and feed it into real time applications with access to information from every possible input, structured, unstructured et cetera.
Operator: Our next question comes from the line of Brad Reback with Stifel. Please proceed with your question.
Brad Reback: Matt, what if anything needs to change at Capella in order for it to be a true volume customer acquisition engine?
Matt Cain: I think we’re heading that direction. We’re excited to spend time next week. We’re going to talk a lot about this idea that Capella is at the inflection point. I think the product that we have, the usability, the integration of things like IQ is going to give us the opportunity to augment our highly sophisticated enterprise go-to-market motion and benefit from more product led growth. And I think we’re to the point now where we have the solution. We’re marrying that up with know, how we’re marketing, how we’re investing in go-to-market resources, how we’re thinking about partnerships. And so, I do think we’re at the point of that you know hockey stick where we’re going to start to see that inflection coming.
Operator: Our next question comes from the line of Taz Koujalgi with Wedbush Securities. Please proceed with your question.
Taz Koujalgi: I have a question on Capella first one. So you’re very positive on the new logo traction driven by Capella. Can you comment on the deal sizes or deal values of these Capella deals by comparing to enterprise new logo adds? Are these smaller? Are these at the same level of the enterprise new deals?
Greg Henry: Yes. Taz, it’s Greg. Thanks for the question. Yes, the new logos in Capella are certainly smaller than what we would see on the enterprise side of the business. But again, the time to value and again, we’re going to talk about this a lot next week is the growth rate on that is substantial compared to the enterprise business. So, they start smaller but they grow faster and that we always assume that would be the nature of the Capella model. Now that said, there are times where we get larger ones, but in general they’re smaller than the enterprise part of the business.
