Development environment – Now that you have delivered a fully configured data environment to the product (or services) team in your organization, the data scientists have started working on it. They are using the data science notebook interface that they are familiar with to do exploratory analysis. The data engineers have also started working in the environment and they like working in the context of their IDEs. They would prefer a connection between their favorite IDE and the data environment that allows them to use the familiar interface of their IDE to code and, at the same time, use the power of the data environment to run through unit tests, all in context of their IDE.
Any disciplined engineering team would take their code from the developer’s desktop to production, running through various quality gates and feedback loops. As a start, the team needs to connect their data environment to their code repository on a service like git so that the code base is properly versioned and the team can work collaboratively on the codebase.
This is more of a conceptual post than a direct how-to guide, but it does a good job of getting you on the right path.