Kent Graziano

workflow. It's been very interesting for me because I come from a traditional relational background with third normal form modeling as well as dimensional modeling and data vault modeling in the data warehouse world. So looking at snowflake and the features that we have even just looking At the, the semi structured data, so whether it's JSON, Avro parkay, things like that, a lot of our customers have found the they can load that into a column. So effectively, you have a table now, where there's maybe a couple of metadata columns and a variant column. And then he single row in that table that in that variant, you'll have an entire JSON document that might have arrays and nested arrays and all sorts of really interesting structures within the we'll call it the schema of the JSON document. And our customers are able to build views on that using our JSON SQL syntax, and make it look like a dimensional model. And I found this incredibly fascinating and also incredibly powerful. In that, we're now able to do things like load web logs into a database table, but make it accessible to I'll say your average business analyst in a dimensional format. So they can actually do analytics on this type of data now, using Tableau using Power BI using tools that they're very familiar with. And they don't even know that it's JSON data under the covers. And so the combination of being able to load that type of data, the sequel extensions to access it, we've actually eliminated the T now in the ELT extract, load transform, the transform part is now a sequel view.

And it's transforming the structure into something that business analyst understands. But we're able to then load data much faster and really reduce the latency and the time to value for that data and the performance optimizations that are built into snowflake combined with our ability to do these independent compute clusters, which we call virtual warehouses allows us to get the performance out of that. So there there is there's literally no indexing, there's no manual partitioning, there's no distribution keys. And so DBAs are not having to spend, you know, I'll say nights and weekends trying to make make this stuff perform, we're able to just build views on it, explosive, the tableau, and the business analysts now have rapid access to all of this data. And in Additionally, we can allow them to join that semi structured data now to structured data that might have been loaded, say from a CRM system in a more traditional scheme, from a data modeling perspective. There's two factors here with snowflake one in the scenarios we're talking about right now with the semi structured data is we're doing almost virtual mock Right, the modeling approaches, whether it's three nf, data vault, or dimensional can all be done with fuse. So it's more about what is the right structure to represent the data in for the business use case. And that also means that you can have one set of data and have multiple representations of it serving different use cases. And this really gets us down to that concept of the single source of truth that we've been trying to reach in the data warehousing world for for several decades. The other aspect of modeling with snowflake is the system was designed to be what we call schema agnostic, where many of the traditional on premises database systems really preferred a specific modeling technique via third normal form or dimensional for the most part, and required specific today To make those things work, snowflake was designed to be performance against any modeling technique, because we wanted to ensure that wherever our customers were coming from in their legacy on prem world, they wouldn't necessarily have to change their approach to modeling in order to get effective use out of snowflake. And so that was actually designed from the ground up. One of the things that I didn't say earlier is our founders wrote snowflake, completely from the ground up, it was a it's a brand new relational database. So not only did they invent a new architecture, to take advantage of the elasticity, and the dynamic nature of the cloud, and to eliminate the concurrency and performance issues that people have had with the traditional systems, they were were able to do this only by starting from ground zero and not forking some previous code base. So it is all new code, resulting in this really dynamic. data platform that can be used for MS, we're talking about data lake types of applications, as well as traditional analytics applications, and B and C sequel compliant as well, at the same time.