Neil Patel

Thank you. Thanks for having me.

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.

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.

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 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. I'm Neil Jagdish Patel, and I'm the co-founder and CEO of Axiom.

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. I'm Neil Jagdish Patel, and I'm the co-founder and CEO of Axiom.

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. I'm Neil Jagdish Patel, and I'm the co-founder and CEO of Axiom.

Axiom is a event store essentially and when you have logs or trace events if you have events being generated from products or your services things like that and what it does is it takes it all in gets it stored for you and makes it immediately queryable however you want and so you may be making charts and analytics you may be just looking at that data raw or you may want to do something with that and you know export it somewhere else and

Axiom is a event store essentially and when you have logs or trace events if you have events being generated from products or your services things like that and what it does is it takes it all in gets it stored for you and makes it immediately queryable however you want and so you may be making charts and analytics you may be just looking at that data raw or you may want to do something with that and you know export it somewhere else and

Axiom is a event store essentially and when you have logs or trace events if you have events being generated from products or your services things like that and what it does is it takes it all in gets it stored for you and makes it immediately queryable however you want and so you may be making charts and analytics you may be just looking at that data raw or you may want to do something with that and you know export it somewhere else and

So essentially what we've been doing is building out the data store, which is brand new and it's something we built ourselves. And the whole idea is that so much of an organization's data is actually held in events. And those are the things that are happening with a timestamp.

So essentially what we've been doing is building out the data store, which is brand new and it's something we built ourselves. And the whole idea is that so much of an organization's data is actually held in events. And those are the things that are happening with a timestamp.

So essentially what we've been doing is building out the data store, which is brand new and it's something we built ourselves. And the whole idea is that so much of an organization's data is actually held in events. And those are the things that are happening with a timestamp.

And they actually bring up the history of what you do, whether it's a service doing it, whether it's a person doing it, etc. And so what Axiom does is allow you to store all of that for a really long time and then bring value out of that, whether it's for log analysis, tracing, whether it's for product analytics or anything related like that.

And they actually bring up the history of what you do, whether it's a service doing it, whether it's a person doing it, etc. And so what Axiom does is allow you to store all of that for a really long time and then bring value out of that, whether it's for log analysis, tracing, whether it's for product analytics or anything related like that.

And they actually bring up the history of what you do, whether it's a service doing it, whether it's a person doing it, etc. And so what Axiom does is allow you to store all of that for a really long time and then bring value out of that, whether it's for log analysis, tracing, whether it's for product analytics or anything related like that.

We actually started off with less on the data store side and more on the side of trying to build out more of like event tooling. So the connectivity piece around how do you make sense of all this data that's caught up in silos. So at the time, the data stores would allow you to have about 15 days, 30 days of data before it started getting really expensive, etc.,

We actually started off with less on the data store side and more on the side of trying to build out more of like event tooling. So the connectivity piece around how do you make sense of all this data that's caught up in silos. So at the time, the data stores would allow you to have about 15 days, 30 days of data before it started getting really expensive, etc.,

We actually started off with less on the data store side and more on the side of trying to build out more of like event tooling. So the connectivity piece around how do you make sense of all this data that's caught up in silos. So at the time, the data stores would allow you to have about 15 days, 30 days of data before it started getting really expensive, etc.,

And we were thinking and working on if companies want to do something with this, how do they build out event systems on top or have reactive states on top and things like that.