I've done a handful of interviews recently where the 'scaling' problem involves something that comfortably fits on one machine. The funniest one was ingesting something like 1gb of json per day. I explained, from first principals, how it fits, and received feedback along the lines of "our engineers agreed with your technical assessment, but that's not the answer we wanted, so we're going to pass". I've had this experience a good handful of times.

I think a lot of people don't realize machines come with TBs of RAM and hundreds of physical cores. One machine is fucking huge these days.

I agree - and it's not just what gets you promoted, but also what gets you hired, and what people look for in general.

You're looking for your first DevOps person, so you want someone who has experience doing DevOps. They'll tell you about all the fancy frameworks and tooling they've used to do Serious Business™, and you'll be impressed and hire them. They'll then proceed to do exactly that for your company, and you'll feel good because you feel it sets you up for the future.

Nobody's against it. So you end up in that situation, which even a basic home desktop would be more than capable of handling.

I've spent my last 2 decades doing what's right, using the technologies that make sense instead of the techs that are cool on my resume.

And then I got laid off. Now, I've got very few modern frameworks on my resume and I've been jobless for over a year.

I'm feeling a right fool now.

> datasets that often fit entirely in RAM.

Yep, and a lot more datasets fit entirely into RAM now. Ignoring the recent price spikes for a moment, 128GB of RAM in a laptop is entirely achievable and not even the limit of what is possible. That was a pipe dream in 2014 when computers with only 4GB were still common. And of course for servers the max RAM is much higher, and in a lot of scenarios streaming data off a fast local SSD may be almost as good.

I’ve seen this pattern play out before. The pushback on simpler alternatives seems from a legitimate need for short time to market from the demand some of the equation and a lack of knowledge on the supply side. Every time I hear an engineer call something hacky, they are at the edge of their abilities.

Yep. The cloud providers however always get paid, and get paid twice on Sunday when the dev-admins forget to turn stuff off.

It’s the same story as always, just it used to be Oracle certified tech, now it’s the AWS tech certified to ensure you pay Amazon.

Worse in some ways, better in others. DuckDB is often an excellent tool for this kind of task. Since it can run parallelized reads I imagine it's often faster than command line tool, and with easier to understand syntax

Well. I try for a middle ground. I am currently ditching both airflow and dbt. In Snowflake, I use scheduled tasks that call stored procedures. The stored procedures do everything I need to do. I even call external APIs like Datadog’s and Okta’s and pull down the logs directly into snowflake. I do try to name my stored procedures with meaningful names. I also add generous comments including urls back to the original story.

For a dasaset that live in RAM, the best solution are DuckDB or clickhouse-local. Using SQLish data is easier than a bunch of bash script and really powerful.

On the contrary, the key message from the blog post is not to load the entire dataset to RAM unless necessary. The trick is to stream when the pattern works. This is how our field routinely works with files over 100GB.

This reminds me of this reddit comment from a long time ago: https://www.reddit.com/r/programming/comments/8cckg/comment/...

> a robust bash script

These hardly exist in practice.

But I get what you mean.

Airflow and dbt serve a real purpose.

The issue is you can run sub tib jobs on a few small/standard instances with better tooling. Spark and Hadoop are for when you need multiple machines.

Dbt and airflow let you represent your data as a DAG and operate on that, which is critical if you want to actually maintain and correct data issues and keep your data transforms timely.

edit: a little surprised at multiple downvotes. My point is, you can run airflow and dbt on small instances, and you can do all your data processing on small instances with tools like duckdb or polars.

But it is very useful to use a tool like dbt that allows you to re-build and manage your data in a clear way, or a tool like airflow which lets you specify dependencies for runs.

After say 30 jobs or so, you'll find that being able to re-run all downstreams of a model starts to payoff.

happy middle ground: https://www.definite.app/ (I'm the founder).

datalake (DuckLake), pipelines (hubspot, stripe, postgres), and dashboards in a single app for $250/mo.

marketing/finance get dashboards, everyone else gets SQL + AI access. one abstraction instead of five, for a fraction of your Snowflake bill.

"I've seen startups burning $5k/mo on distributed compute clusters to process <10GB of daily logs, purely because setting up a 'Modern Data Stack' is what gets you promoted, while writing a robust bash script is seen as 'unscalable' or 'hacky'."

Also seen strange responses from HN commenters when it's mentioned that bash is large and slow compared to ash and bash is better suited for use as an interactive shell whereas ash is better suited for use as a non-interactive shell, i.e., a scripting shell

I also use ash (with tabcomplete) as an interactive shell for several reasons

If airflow is a layer of abstraction something is wrong.

Yes it is an additional layer, but if your orchestration starts concerning itself with what it is doing then something is wrong. It is not a layer on top of other logic, it is a single layer where you define how to start your tasks, how to tell when something is wrong, and when to run them.

If you don't insist on doing heavy compitations within the airflow worker it is dirt cheap. If it's something that can easily be done in bash or python you can do it within the worker as long as you're willing to throw a minimal amount of hardware at it.

Here’s the thing though, most companies work with small data. The distribution of data set size follows a power law which means that few engineers get to work with petabyte sized datasets. That said, the job market definitely incentivizes people to have that kind of experience on their resume if they want to keep progressing in salary. This incentivizes over engineering.

Absolutely, when I worked at (semi-well-known unicorn) a half-dozen years ago on the data-engineering team the manager told me "Hey we want to use spark next quarter, that's a huge initiative."

And I immediately asked, "in what capacity?" And the answer was don't-know/doesn't-matter, it's just important that we can say we're using it. I really wish I understood where that was coming from (his manager resume-building? somebody getting a kickback?)

> I rewrote some Bash and Python scripts into C# that were previously processing gigabytes of JSON

Hah, incredibly funny, I remember doing the complete opposite about 15 years ago, some beginner developer had setup a whole interconnected system with multiple processes and what not in order to process a bunch of JSON and it took forever. Got replaced with a bash script + Python!

> Just how much data do you need when these sort of clustered approaches really start to make sense?

I dunno exactly what thresholds others use, but I usually say if it'd take longer than a day to process (efficiently), then you probably want to figure out a better way than just running a program on a single machine to do it.

I remember a panel once at a PyCon where we were discussing, I think, the anaconda distribution in the context of packaging and a respected data scientist (whose talks have always been hugely popular) made the point that he doesn't like Pandas because it's not excel. The latter was his go to tool for most of his exploratory work. If the data were too big, he'd sample it and things like that but his work finally was in Excel.

Quick Python/bash to cleanup data is fine too I suppose and with LLMs, it's easier than ever to write the quick throwaway script.

I like the peer comment's answer about a processing time threshold (e.g., a day). Another obvious threshold is data that doesn't conveniently fit on local disks. Large scale processing solutions can often process directly from/to object stores like S3. And if it's running inside the same provider (e.g., AWS in the case of S3), data can often be streamed much faster than with local SSDs. 10GB/s has been available for a decade or more, and I think 100GB/s is available these days.

How do you stream parse json? I thought you need to ingest it whole to ensure it is syntactically valid, and most parsers don't work with inchoate or invalid json? Or at least it doesn't seem trivial.

> Just how much data do you need when these sort of clustered approaches really start to make sense?

I did not see your comment earlier, but to stay with Chess see https://news.ycombinator.com/item?id=46667287, with ~14Tb uncompressed.

It's not humongous and it can certainly fit on disk(s), but not on a typical laptop.

It's not about how much data you have, but also the sorts of things you are running on your data. Joins and group by's scale much faster than any aggregation. Additionally, you have a unified platform where large teams can share code in a structured way for all data processing jobs. It's similar in how companies use k8s as a way to manage the human side of software development in that sense.

I can however say that when I had a job at a major cloud provider optimizing spark core for our customers, one of the key areas where we saw rapid improvement was simply through fewer machines with vertically scaled hardware almost always outperformed any sort of distributed system (abet not always from a price performance perspective).

The real value often comes from the ability to do retries, and leverage left over underutilized hardware (i.e. spot instances, or in your own data center at times when scale is lower), handle hardware failures, ect, all with the ability for the full above suite of tools to work.

Adam Drake's example (OP) also streams from disk. And the unix pipeline is task-parallel.

> But I am not sure they ever actually worked with analysing data more than using it as a log parser.

It really feels that way. Real data analysis involves a lot more than just grepping logs. And the reason to be wary of starting out unprepared for that kind of analysis is that migrating to a better solution later is a nightmare.

If you get all the data on fast SSDs in a single chassis, you probably still beat EMR over S3. But then you have a whole dedicated server to manage your 14TB of chess games.

The "EMR over S3" paradigm is based on the assumption that the data isn't read all that frequently, 1-10x a day typically, so you want your cheap S3 storage but once in a while you'll want to crank up the parallelism to run a big report over longer time periods.

Probably not.

The compressed data can fit onto a local SSD. Decompression can definitely be streamed.

The benefit of prestodb is that it can be used without even starting one of these expensive instances in AWS Athena.

https://www.scylladb.com/2019/12/12/how-scylla-scaled-to-one...

I like this one where they put a dataset on 80 machines only then for someone to put the same dataset on 1 Intel NUC and outperform in query time.

https://altinity.com/blog/2020-1-1-clickhouse-cost-efficienc...

Datasets never become big enough…

And we can have pretty fucking big single machines right now