Time-varying entities may contain multiple time-varying and static attributes, making mapping them a particular challenge. Time is notorious in modeling tasks. Indeed, the temporal aspect exacerbates the complexity of the modeling task, making simple diagrams look pretty complex. The temporal dimension becomes particularly nasty when it takes part in identifying entities. The figure on the right visualizes the typical database example.
From time to time throughout my career, I have been involved in projects with dramatic releases when we built and delivered something very new and very special. The release of InfluxDB Cloud, powered by IOx (referred to as “InfluxDB IOx” for short below) absolutely meets those criteria. I want to explain my personal views of why this release is so impactful and why I am so excited to be part of it.
In different techniques, entities and relationships remain central. However, their nature and roles are reinterpreted according to the business goals. Data modeling is the process of defining and representing the data elements in a system in order to communicate connections between data points and structures. In his impactful book “Designing Data-Intensive Applications,” Martin Kleppmann describes data modeling as the most critical step in developing any information system.
