Data democratization for Marketing using StreamSets, Snowflake and Software AG

Speakers: Daniel Adayev, Marketing Operations Manager

Daniel Adayev: Hi, my name is Daniel Adayev. I am the Marketing Operations Manager at StreamSets. And my day-to-day mainly involves system integration, API management and hygiene of the martech stack which primarily involves Marketo and Salesforce. I also work with Sales in order to enable any sort of technology or data into the tech stack that is needed to make them the best sellers they can be.

I would say this question is twofold. Snowflake was massive in forming a foundation for building these projects because this data data was solid before we really had no way of knowing where to start. It was pieced out in different warehouses. As mentioned before, we had limited visibility and Snowflake really helped us migrate all the data into one area. So when we were pulling queries or we had to build out an origin for where to start grabbing the data, it was a question of what database, what table do we pull from Snowflake, rather than what data warehouse do we have to start looking for? What authentication do we have to start using? The process of grabbing that data became much, much easier StreamSets was really big in especially our first project where we were syncing up our data between Marketo and our product platform. Due to the fact that we had multiple ways of entry into the platform, one was through filling out a form on our website, one was through backend invitation by a Solutions Engineer or someone in DevOps. In another way it was being directly by another user, and then there was entering through Snowflake Partner Connect. We just weren't capturing all of those nodes and there was some data loss along the way. Even if somebody signed up through our website, all this stuff was getting all these, all this data was getting picked up on the back end of the product data warehouse. However, we were not able to pick it up. And so we were, we had built a StreamSets Data Collector that took all of that product user data, the emails, titles, the company, first name, last name, and pushed it over into Marketo to make sure that both of those were synced up and aligned. Afterwards, we had the secondary project of pushing in our product activities or platform activities. So we were able to track individual events done by users - when they created a job, or they created a pipeline, they created an environment, they created an engine, if they ran a job - all of those, we were able to pick up and push into Salesforce as individual user activities. And we were able to do that through webMethods using the Snowflake table that it was getting pushed into. And one of the ways that we were populating this master table of product events is we were using Transformer for Snowflake to grab different event activities from different sources. We were then transforming those events. We were standardizing them and then pushing them into this one table that webMethods was then picking up and using.