A Data Warehouse (DWH) is generally a substantial investment. Its whole point is to host and provide access to huge swaths of data, which needs to be loaded from external sources first, and if it is to be of any use, needs to be transformed into datasets that make sense and provide value to end users. Though it might not seem like a big deal at the beginning, it might end up being a quite costly investment – both in terms of money and brainpower – one that is difficult to swallow, especially when you are a bootstrapped startup.
In this talk, we’ll present the experience of building a (modern) Data Warehouse (or Lakehouse?) at Slido, an audience interaction platform which provides Q&A and Polling for varied audiences. It will be discussed from the point of view of a data scientist/engineer who joined Slido with the plan to revolutionalize audience interaction with the help of Machine Learning and ended up building a Data Warehouse instead, as that’s what it turned out the company needed the most. We’ll go over the technological, organizational as well as the people aspect of the process and share all the ups and downs Slido experienced, along with quite a few lessons learned – all the while operating on a shoestring budget.
Whether you are currently contemplating a similar project, have inherited a messy warehouse and wonder how it came to be, or have already been through a similar experience, do not hesitate to come! We’ll surely have a lot of interesting things to share.
Marek stumbled upon AI as a teenager when building soccer-playing robots and quickly realized he is not smart enough to do all the programming by himself. Since then, he's been figuring out ways to make machines learn by themselves, particularly from text. When trying to apply some of these at Slido, an audience interaction platform, he accidentally found himself building a Data Warehouse and beco...