Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie.

So what abstractions do we have as of today? For example, let’s take the resource abstraction (Dagster, Prefect, referred to as an operator in Airflow). You abstract complex environments and connections away with a simple construct like that. You have the immediate benefits of defining that once and using it in every task or pipeline with context.r... See more

What’s more, since data flows that are implemented with pure functions are relatively easy to manage, they largely avoid the need for more complex architecture to manage them. This is the ultimate spirit of Post-Architecture: To use simple, straight-forward tools to solve our problems so that we can not only postpone potentially risky architectural... See more

The last core data stack tool is the orchestrator. It’s used quickly as a data orchestrator to model dependencies between tasks in complex heterogeneous cloud environments end-to-end. It is integrated with above-mentioned open data stack tools. They are especially effective if you have some glue code that needs to be run on a certain cadence, trigg... See more

Several engineers also maintained fallback models for reverting to: either older or simpler versions (Lg2, Lg3, Md6, Lg5, Lg6). Lg5 mentioned that it was important to always keep some model up and running, even if they “switched to a less economic model and had to just cut the losses.” Similarly, when doing data science work, both Passi and Jackson... See more