Data science notebooks, a popular document format used for publishing code, results, and explanations in readable and executable form, broke new ground by combining an ongoing narrative with interactive elements and displays. The result was a new way to capture and transfer knowledge about the process of discovering insights. By studying why data science notebooks have worked so well, we can understand more about related areas with similar characteristics, such as Technology Operations (TechOps).
At first glance, many of the attributes of data science notebooks also apply to TechOps. However, the data scientist and TechOps cohort have different objectives. A data scientist is interested in variable results based on changing elements within queries. A TechOps team responsible for complex operational systems looks for variables and patterns, seeks to understand the root cause, and takes corrective action. Data science notebooks are conducive to instruction and are easy to change. However, in a production operations setting, things need to be repeatable rather than variable. To align with the different user needs in TechOps, the notebook concept evolved into runbooks.