Once upon a time, there were just two data pipelines - a data warehouse to support analysis activities, and a reporting pipeline that produced some strange numbers for investors' board meetings, and all was good. But in today's data wild west, we are getting more demands - from business users who would like to transform their idea into a POC, deploy ML models in production, and ensure quality in the data pipeline with regression and anomaly detection. Keeping up with these challenges requires an agile, automated, and cost-effective operation that isn't always part of our data team's responsibility. On the other hand, this may sound familiar with the DevOps challenges we face daily in a SaaS team? so let's add some DevOps to the rescue. I'm sure Gandalf's famous "you should not pass" comes into your mind rejecting this idea, but let's give it a try with a practical, informal introduction to DataOps.