Before Validio, we were using Great Expectations to manage our data validation and try to catch anomalies and bad data early. However, it was very cumbersome. It was very slow, took a lot of time, and you had to have a lot of assumptions about the data to be able to say how many rows you expect, columns you expect, categories, etc. Not only is generating these assumptions difficult when you are only starting with sample data, but getting to the know the data well enough to have these kind of assumptions also takes a lot of time. This really made it difficult for our engineers to set up good validation rules. So a lot of the time, our pipelines would fail because we were setting up too stringent rules. Or on the other hand, we weren't setting up stringent enough rules and then we weren't catching the problems early enough. With Validio, we have none of that anymore. It just works!

Prior to Validio, we used a popular OSS tool for data validation, but it was slow and required detailed data assumptions, making it challenging for engineers to establish effective validation rules. Validio stood out as a clear upgrade by learning from the underlying data at a very deep level, taking into account things like seasonality, to save precious engineering time. The speed with which Validio can process our data and deliver results is tremendous. It can do 100s of millions of records a minute. For a company with our scale and SLAs it’s hugely valuable. The sooner we know there’s an issue with our data the better. One thing that impresses me with Validio is the ease of use to set it up. We’re using the API to connect, set up a standard of testing and build it into the core of our products. Validio has really good configurality. We're using the API and it's really easy for our product team to connect that in some standardized checks and really build that into the core of our product Validio produced really good results in detecting anomalies, both in overall drift, and drift by segments. Validio can automatically look at this data, account for seasonality, learn trends, and not bug us with a lot of noise or issues related to typically seasonality problems The partnership is very strong and it's been a great process, and they've been great collaborators