Keith: And what problems are you solving? Obviously, if there were no issues to fix it, they wouldn't have started the company. Matt: A few things. First, reach the data. We are a powerful engine for querying data from data lakes.
However, in many cases the challenges are: How do I put my data in one place? Connecting to different sources can enhance that process. Especially in AI, the effectiveness of the model depends on the quality of the data being retrieved.
We consider ourselves as the fuel that powers AI. It also allows experimental and production level use without focusing everything. You can experiment quickly and move to production faster. Uniquely, it supports both cloud and on-prem environments.
Therefore, you can cross the clouds and reach the entire region. Keith: Was the growth of cloud computing one of the main reasons why data began to become so fragmented? Matt: That's part of it. The company has moved to the cloud, but the legacy systems still have on-prem.
Some keep their data on-prem for privacy and security needs. In an M&A scenario, you may use your data in Google Cloud to get another company while using AWS. There are many reasons for fragmentation. Keith: So, what problems will the company face if they weren't using Starburst?
MATT: They probably need more tools and rely heavily on the ETL process to move everything to one place. ETL has that role, but it is not a requirement. You can connect directly to the data source, experiment, and decide whether to move later.
This allows less complexity to be faster. Keith: It's okay, jump into the demo and see what you have. Matt: Yes, absolutely. Let's jump over here. In this demonstration, they pretend to be an airline or travel agency. There is data across a variety of sources.
