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Comment by scapecast | Hacker News Reader

Literally 5 minutes ago I made a comment on LinkedIn on a Terraform Redshift provider how there's a need for a "Terraform for Analytics Infrastructure", where you define e.g. tables and the column names. And then also include everything that happens before and after the warehouse.

I think it would sell like hotcakes.

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Standardized code generation in general is a huge opportunity. My preferred solution would be for everything to speak protobufs natively, and then you wouldn't need to do any other generation - you'd do what they do internally at Google and have tables with 2 columns (one for the key, and one for a fat protobuf that holds all the actual data), file formats like RecordIO as the default pipeline building block, and Capacitor [1] for columnar storage. But in the absence of good query syntax and columnar file formats that can handle rich data types, code generation it is - take the proto file, flatten it out (this is the tricky part, if you have repeated field names in a nested object model), and then you can generate all kinds of stuff from that:

* A table/column schema, which you can automatically synchronize into any DB backend you want via plugins

* Read/write logic in various languages - not an ORM, but a struct that represents a single row and handles the boilerplate.

* Maybe some kinds of richer query/join logic? If you go too far this becomes another ActiveRecord, but I think there's a middle ground.

* Batch pipelines with standard semantics - sort of like a materialized view, but computed via your big-data pipeline of choice rather than in-engine. Imagine that you have table A with 30 fields, table B with 15 fields, and you want to generate a downstream table with all 45. I think it's feasible to have a composable, declarative syntax that lets you create the 45-column table plus the pipeline that populates it with about 3 lines of configuration. Hard to turn into a product, because so much of that pipeline will depend on org-specific tech stack choices, but at the limit the "data platform engineer" could be entirely automated out of 80% of their job (and therefore be able to focus on more interesting things).

[1] https://cloud.google.com/blog/products/bigquery/inside-capac...

If you haven't come across it yet, DBT (data build tool) is a nice solution to the later parts of the pipeline (once you have the raw source data somewhere) https://www.getdbt.com/

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