Data collection and storage are a large component of almost all software projects. Even though most software projects include a data component, this topic is rarely discussed in the DevOps community. The adoption rate of database continuous delivery (CD) is about half the rate of application CD. There are several reasons for this, but the primary one is that databases rarely change as often as applications do. There may be a few model changes, but generally there are no major architectural changes that occur in relation to the database level of your software. Many DevOps practitioners thus do not spend the time to provide continuous delivery of their data storage solutions, which became very apparent when our team was recently tasked to solve a complex problem. In this blog post, I will explore the application of DevOps principles to a data science project.