Neil Patel

We want to do it, but we don't have enough data for what you're trying to achieve. And when we do all the costings and stuff, it's not going to work out for us to have enough to make the second part viable. And then you talk to them a little bit more and they say, but what are you using for your data store? How come I didn't have to connect you to anything?

We want to do it, but we don't have enough data for what you're trying to achieve. And when we do all the costings and stuff, it's not going to work out for us to have enough to make the second part viable. And then you talk to them a little bit more and they say, but what are you using for your data store? How come I didn't have to connect you to anything?

We want to do it, but we don't have enough data for what you're trying to achieve. And when we do all the costings and stuff, it's not going to work out for us to have enough to make the second part viable. And then you talk to them a little bit more and they say, but what are you using for your data store? How come I didn't have to connect you to anything?

Why am I sending everything and being able to read it back so easily? And then the penny dropped and we quickly realized that's the thing we should be working on first and make that viable. So that was the first MVP. It caused the pivot to happen within weeks, essentially, of us giving it to people to try out.

Why am I sending everything and being able to read it back so easily? And then the penny dropped and we quickly realized that's the thing we should be working on first and make that viable. So that was the first MVP. It caused the pivot to happen within weeks, essentially, of us giving it to people to try out.

Why am I sending everything and being able to read it back so easily? And then the penny dropped and we quickly realized that's the thing we should be working on first and make that viable. So that was the first MVP. It caused the pivot to happen within weeks, essentially, of us giving it to people to try out.

And then the second one was about actually 18 months after that when we released our beta cloud around our data store. So we took this demo data store and we made it real during that time. And so that was the second MAP.

And then the second one was about actually 18 months after that when we released our beta cloud around our data store. So we took this demo data store and we made it real during that time. And so that was the second MAP.

And then the second one was about actually 18 months after that when we released our beta cloud around our data store. So we took this demo data store and we made it real during that time. And so that was the second MAP.

It was interesting because you have already built a bunch of things up from the front end to API services back down to how those API services talk to a data store. So the new part was the data store. The old parts were the API server and the front end, which we were repurposing. The reality was the data store we were building was very novel in architecture.

It was interesting because you have already built a bunch of things up from the front end to API services back down to how those API services talk to a data store. So the new part was the data store. The old parts were the API server and the front end, which we were repurposing. The reality was the data store we were building was very novel in architecture.

It was interesting because you have already built a bunch of things up from the front end to API services back down to how those API services talk to a data store. So the new part was the data store. The old parts were the API server and the front end, which we were repurposing. The reality was the data store we were building was very novel in architecture.

And at the time, we felt like as long as we did these three things, we would have it done within a certain number of months, basically. And we were trying to go for the ingest will be brand new and we'll do it like this. Storage will only use object storage. Queries will only use serverless.

And at the time, we felt like as long as we did these three things, we would have it done within a certain number of months, basically. And we were trying to go for the ingest will be brand new and we'll do it like this. Storage will only use object storage. Queries will only use serverless.

And at the time, we felt like as long as we did these three things, we would have it done within a certain number of months, basically. And we were trying to go for the ingest will be brand new and we'll do it like this. Storage will only use object storage. Queries will only use serverless.

And exactly how you'd expect engineers to behave, we were like, and we'll get it done within six months, right? Or eight months or whatever it was. The reality, though, was that for the API and the front-end side, we just ate the tech debt and we just repurposed it to make it work in that timeframe. What happened was essentially we had made a data store.

And exactly how you'd expect engineers to behave, we were like, and we'll get it done within six months, right? Or eight months or whatever it was. The reality, though, was that for the API and the front-end side, we just ate the tech debt and we just repurposed it to make it work in that timeframe. What happened was essentially we had made a data store.

And exactly how you'd expect engineers to behave, we were like, and we'll get it done within six months, right? Or eight months or whatever it was. The reality, though, was that for the API and the front-end side, we just ate the tech debt and we just repurposed it to make it work in that timeframe. What happened was essentially we had made a data store.

And so there's endless things on the other side of you making that viable, as well as we needed our AWS. There were problems like the bandwidth between S3 and Lambda. We were getting caught on that constantly. So we were hitting the limits there. So you could get all the data in, get it into S3, but we couldn't query it fast enough because we just basically saturate everything.

And so there's endless things on the other side of you making that viable, as well as we needed our AWS. There were problems like the bandwidth between S3 and Lambda. We were getting caught on that constantly. So we were hitting the limits there. So you could get all the data in, get it into S3, but we couldn't query it fast enough because we just basically saturate everything.