Transforming Purpose Financial’s Data Strategy with Snowflake and AWS

This transcript was automatically generated.

John Young. I'm the VP of data at purpose financial purpose was created to make sure that our customers could have good financial health for us. It's really gross automation, making sure we can do it in a customer friendly way, and then move on to new products and services. When the company, chose to move to the cloud, they were pretty adamant they wanted AWS as a partner, and they wanted Snowflake as a partner.

From a security and governance perspective, Snowflake has, native capabilities built in. We need to make sure we're doing the transactions and protecting them as we go. So you have things like data clean rooms, which are also important for a company like ours because of some of the partnerships we have with some data vendors, data vendors, which also have to protect PII from their customers. And so that sort of accessibility part that Snowflake provides is super important to us.

Really, we went with Snowflake for speed, scale, security, and that helps us drive automation and self-service. We use, snowflake as our single source of truth. That means that over the years, we've collected different entity IDs for different customers, consolidating that kind of stuff and making sure it's it's all in one place. We also use AWS Dynamo as a back end database for web applications because the speed and the interaction there.

Part of our log tracking on the web, we actually use Kinesis Firehost to stream that into an area where Snowpipe picks it up and we drop it. With AWS and Snowflake, purpose financial has been able to increase the speed, improve the scale and security for detecting fraud across our platforms and across our customer base. The role of AI and and the capabilities that snowflakes provide. We are in this sort of transitional phase, but we've got tons of use cases, anywhere from the document AI piece.

From what I've seen about Horizon is the purpose is in this position of trying to transform governance.

And to be able to have that, you know, automation embedded within the system of truth for us. For me, that's very exciting. For us to be able to go in to reengage from a recommendation engine perspective. So that's important. So Cortex will be important for those kind of things. The containerization process where you bring another pipe Python jobs, very important because that's also with speed scale security. And then just the ability to show it using streamlet or a SnowSight.

There's some really amazing things that you can do very quickly. And again, I go back to a previous statement which is Snowflake's kinda doing everything for you.