Theme
Description
By providing blocks and pipes as first-level programming constructs and following the pipes and filters architectural approach, Jayvee aligns with the mental model of an ETL pipeline as a graph of processing steps that data flows through. This mental model is also used in many visual programming tools for data pipelines as it is how users commonly visualize a data pipeline.
With the close match between language elements and domain concepts, previous experience in data engineering or working with other data pipeline tools can also be used to understand a data pipeline from source code. As a result, previous knowledge from working with data by subject-matter experts without software engineering experience is also applicable to understanding data pipelines written in Jayvee (see also PL6.2 - Domain-specific language elements like blocks make Jayvee easy to understand).
This close match with the mental model of ETL pipelines must be carefully maintained however. If it is disrupted, understandability is reduced again. In this case, including elements on a lower abstraction level than readers think of has confused participants (see PL3.3 - Interpretation blocks make outcome unclear).
Representative Quotes
Names of the blocks are familiar with the concepts that are usually used in the data pipelines.
- S17
Very human like thinking of how to program an ETL pipeline.
- S31
Jayvee code steps are directly mapped to the data engineering pipeline lifecycle.
- S35
Since we have a syntax that very well shows the actual flow of the pipeline (via the block → block → … syntax), it also easily understandable what blocks are executed in which order. (…) Jayvee is specialized to represent pipelines, which shows. The syntax is adapted to very well represent a sequential flow, which is easily readable, just by looking at it.
- S37
Jayvee looks much more focused on building and working with pipelines than Python as Python is a multi purpose language.
- S53