Data engineering was software engineering from the very beginning. Then a bunch of business analysts who didn't know anything about writing software got jealous and said that if you knew SQL/DBT you were a data engineer. I've had to explain too many times that yes, indeed, I can set up a CI/CD pipeline or set up kafka or deploy Dagster on ECS, to the point where I think I need to change my title just to not be cheapened.

The comments here are... interesting, as they indicate a strong split between analysts and those engineers that can operationalize things. I see another dimension to it all.

My title is senior data engineer at GAMMA/FAANG/whatever we're calling them. I have a CS degree and am firmly in the engineering. My passion, though, is in using software engineering and computer science principles to make very large-scale data processing as stupid fast as we can. To the extent I can ignore it, I don't personally care much about the tooling and frameworks and such (CI/CD, Airflow, Kafka, whatever). I care about how we're affinitizing our data, how we index it, whether and when we can use data sketches to achieve a good tradeoff between accuracy and compute/memory, and so on.

While there are plenty of folks in this thread bashing analysts, one could also bash other "proper" engineers that can do the CI/CD but don't know shit about how to be efficient with petabyte-scale processing.

I've never really seen the distinction between data and software engineering. It's more like front-end vs backend. If you're a data engineer and it's all no code tooling, then you're just an analyst or something.

"Data engineering and software engineering are converging" says firm selling analytics products/services. I think the perspective here may be a bit skewed.

One thing that I don't see mentioned but that does bug me: data engineers often use a lot of Python and SQL, even the ones that have heavily adopted software engineering best practices. Yet both languages are not great for this.

Python is dynamically typed, which you can patch a bit with type hints, but it's still easy to go to production with incompatible types, leading to panics in prod. It's uncompiled nature also makes it very slow.

SQL is pretty much impossible to unit test, yet often you will end up with logic that you want to test. E.g. to optimize a query.

For SQL I don't have a solution. It's a 50 year old language that lacks a lot of features you would expect. It's also the defacto standard for database access.

For Python I would say that we should start adopting statically typed compiled languages. Rust has polars as dataframe package, but the language itself isn't that easy to pick up. Go is very easy to learn, but has no serious dataframe package, so you end up doing a lot of that work yourself in goroutines. Maybe there are better options out there.

It's not hard to do data engineering to the standards of software engineering, and many people do it already, provided that

1. You use a real programming language that supports all the abstractions software engineers rely on, not (just) SQL.

2. The data is not too big, so the feedback cycle is not too horrendously slow.

#2 can't ever be fully solved, but testing a data pipeline on randomly subsampled data can help a lot in my experience.

For the foundation on data engineering I'd recommend this book by Joe Reis and Matt Housley. They did a good job on providing the framework that includes data engineering lifecycle, software engineering, data management, data architecture, etc. You can check the proposed framework here [1],[2].

[1] Fundamentals of Data Engineering:

https://www.oreilly.com/library/view/fundamentals-of-data/97...

[2] Fundamentals of Data Engineering Review:

https://maninekkalapudi.medium.com/fundamentals-of-data-engi...

I think they've been fully converged in most strong practitioners for a long time.

There's a specific type of "data engineer" (quotes to indicate this is what they're called by the business, not to contest their legitimacy) that just writes lots of SQL, but they're usually a bad hire for businesses. They're approximately as expensive as what people call platform engineers, but platform engineers in the data space can usually do modelling as well.

When organizations split teams up by the most SWE-type DEs and the pure SQL ones, the latter all jockey to join the former team which causes a lot of drama too.

Here’s an argument that college freshmen should be introduced to data science and computer science in the same introductory course. They’ve written a textbook, which seems pretty sensible:

https://cs.brown.edu/~sk/Publications/Papers/Published/kf-da...

Maybe. On the one side you have something like dbt or Moosestack. On the other hand analytics and data pipelining is still a lot of no code tooling and I doubt it will go away. However I would love to learn more about how other people use coding agents to do DE tasks.

If are orchestrating pipelines in airflow or Prefect you are having to write the client software around those engines, and its a lot of python.

Another anecdatum: the data engineers role at Zillow is called "Software Development Engineer, Big Data"

This split between the main app stack and the data engineering / analytics stack is a time-tested architectural pattern. Has clickhouse changed the game so much that it is no longer helpful to have these purpose-built stacks? With modern coding agents being able to write more faster it might be good to explore more separation and purpose-built stacks and less convergence.

Many data teams often find themselves as 'tool jockeys' instead of becoming true engineers. They primarily learn some company data, and then rely on drag-and-drop or YML configuration functionality within the constraints of the tool's environment.

Their organization often insists they must use standard tools, and their idea of a good job is that the task works fine within their personal version. No automatic testing, no automated deployment, no version control, and handcrafted environments. And then they get yelled at when things break and yelled at for taking too long. And most DEs want to quit the field after a few years.

The real question is not that DE and software engineering are converging. It's why most DEs don't have the self-respect and confidence to engineer systems so that their lives don't suck.

One things have seen through my more recent exposure to experienced data engineers is the lack of repeatability rigor (CI/CD, IaC, etc.). There's a lot of doing things in notebooks and calling that production-ready. Databricks has git (GitHub only from what I can tell) integration, but that's just checking out and directly committing to trunk, if it's in git then we have SDLC right, right? It's fucking nuts.

Anyone have workflows or tooling that are highly compatible with the entrenched notebook approach, and are easy to adopt? I want to prevent theses people from learning well-trodden lessons the hard way.

People who can utilize the tooling to process petabytes of data efficiently aren’t the ones that are catching flack. The people I’m thinking of basically run massive inefficient SQL queries and then throw their hands up when it runs slowly or gets an oom error. They don’t even know how to do an explain plan. And if you try to explain to them things like partitioning, indexes, sketches, etc then they are not able to comprehend and argue that it’s not their job to learn, and that it’s the “proper engineers” job to scale the processing.

>one could also bash other "proper" engineers that can do the CI/CD but don't know shit about how to be efficient with petabyte-scale processing.

But that would be SWEs no?

I was a 'data engineer' (until they changed the terrible title) at a startup and I ended up having to fight with Spark and Apache Beam at times, eventually contributing back to improve throughput for our use cases.

That's not the same thing as a Business Analyst who can run a little pyspark query.

I mean this very sincerely but I'm a little lost how data engineering is distinct from software engineering. It seems like just a subset of it, my title was software engineer and I've done what sounds like very similar work.

If you’re using some variety of spark for your data engineering then scala is an option too.

In general, choice of language isn’t important - again if you’re using spark your data frame structure schema defines that structure Python or not.

Most folks confuse pandas with “data engineering”. It’s not. Most data engineering is spark.

When I was most recently at Google (2021-ish) my team owned a bunch of SQL Pipelines that had fairly effective SQL tests. Not my favorite thing to work on, but it was a productive way to transform data. There are lots of open source versions of the same idea, but I have yet to see them accompanied with ergonomic testing. Any recommendations or pointers to open source SQL testing frameworks?

I've been a professional java dev for a decade. I've written a little python, clojure, lots of JS/TS/Node.

SQL is the most beautiful, expressive, get stuff done language I've used.

It is perfect for whatever data engineering is defined as.