>> Code as ETL was a huge industry shift
No it’s not. Try and see what banks, retail, manufacturing, various large enterprises still use. They need scale, observability, modularity, and maintainability.
No it’s not. Try and see what banks, retail, manufacturing, various large enterprises still use. They need scale, observability, modularity, and maintainability.
> They need scale, observability, modularity, and maintainability.
Seems orthogonal to code-vs-gui dimension.
Once you have GUI ETL tools, in my experience, you can't modularise because the ETL tool makes assumptions about where the boundaries are that are different from what suit the domain in question. Observability falls over because you're now limited to the ETL tool instead of the domain. Scale suffers because now the ETL data model needs to be preserved and high-performance tricks might need the entire tool to be worked around, etc, etc.
Code is the highest-performance environment we have for working with huge complex systems made of if statements and loops. Giving that up to go to a tool doesn't actually yield any advantages; there needs to be an abstraction with huge practical benifits and a DAG isn't it. Modeling a DAG in a true programming language isn't hard enough to justify moving away from an IDE.
An ETL pipeline in practice is still uncomfortably close to a big spaghetti of if-thens and loops, tooling and extra models create patterns that often block a lot of the useful properties you list. The real gains come from not writing a custom scheduler, but splitting out the valuable scheduler from the ETL tool means that you have a scheduler, not an ETL tool. Sometimes there is an ecosystem of adaptors that makes a big difference, but if that doesn't meet your engineering requirements then the tool is useless (because you don't have any real levers to pull on the scale/observability/modularity and maintainability front).
From my experience SQL is basically the lowest common denominator everything speaks and even then the Oracle SQL dialect is subtly different to Microsoft SQL for example - things are subtly different enough it introduces frustrations.
There has been movement in last couple of years to hoist everything into a common "datalake" but my understanding has been that ingestion into this lake is not a simple process by any means and requires batch processes that need demanding compute resources and is slow (i.e. takes many hours and runs over night).
Sounds like an ideal fit for on-prem/co-located systems. The big problem with on-prem is the egress costs from wherever all your data resides.
With on-prem, doubling your hardware doesn't double your ops expenses, so it makes sense, if you already have a server-room, to fill it to capacity.
Node-RED is a common ETL approach in the scenario you described, but I find it too limiting beyond basic examples.
I would also add that in my 25 years in the industry I have never actually come across an ETL workflow that was complex enough that it required people working it in code. Those opinions seem to occur before proper analysis happens.
The reality is that we shouldn't be making the transformation in written data, now you have staging tables, procs, views, resulting tables procs, views, a staging database (often called staging or etl), and an unhappy DBA who yells at you every time you cross-apply incorrectly.
ETL should be done before the data lands in SQL.
I think that's orthogonal to the occasional need to use SQL to extract source data. And it's hard to beat the performance of RDBMS for your transformations. It's almost their whole raison d'être.