The truth is that most "big data" problems aren't big and can often be solved with awk and xargs.
Big data is dead
81–90 of 444 posts
Re: Big data is dead
#82"For more than a decade now, the fact that people have a hard time gaining actionable insights from their data has been blamed on its size." The real issue is that business people usually ignore what the data says. Wading through data takes a huge amount of thought, which is in short supply. Data Scientists are commonly disregarded by VPs in large corporations, despite the claims about being "data driven". Most corpo…
I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs. I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick so…
That's like a portrait artist that finds success by painting people more beautiful than they really are vs a starving one that paints them true to life due to sense of artistic integrity.
Reminds me how Garth Brooks started doing metal after becoming a country music star.
Re: Big data is dead
#83Earlier quoted context omitted.
"customer obsession" was always at the mercy of the real obsession: "making money hand over fist". The former will ALWAYS lose out to the latter given enough cycles.
Yeah, "customer obsession" really just means "market share / growth obsession" which is a means to (eventually) making monopoly profits. Which Amazon seems to have achieved.
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Re: Big data is dead
#84MotherDuck has been making the rounds with a big funding announcement [1], and a lot of posts like this one. As a life-long data industry person, I agree with nearly all of what Jordan and Ryan are saying. It all tracks with my personal experience on both the customer and vendor side of "Big Data".
That being said, what's the product? The website says "Commercializing DuckDB", but that doesn't give much of an idea of what they're offering. DuckDB is already super easy to use out of the box, so what's their value-add? It's still a super young company, so I'm sure all that is being figured out as we speak, but if any MotherDuckers are on here, I'd love to hear more about the actual thing that you're building.
[1]: https://techcrunch.com/2022/11/15/motherduck-secures-investm...
Re: Big data is dead
#85Earlier quoted context omitted.
Presumably the "order" you mention is a primary key to another table, likely one that references the individual items that make up that order, so the data will be much larger than you estimate. It will grow larger still if you include web logs from your e-commerce site and event data from your mobile app so that you can correlate these orders with items that customers considered but ultimately didn't buy. How will yo…
There are functionally less than 1000 organizations that currently require distributed compute for data analysis. You can get off the shelf AWS units with 1000 cores, terabytes of ram and storage, etc. The cost of compute has decreased faster than the amount of data we have to store and process. What we used to do with spark jobs we can do with python on a single box.
Citations please? That's a pretty bold statement to make in the face of observed reality.
Re: Big data is dead
#86There is literally a post on front page on ChatGPT, and Microsoft and Google are preparing to duke it out starting in the _next 2 days_ over big-data generated 'chat' result. Big data was never going to be useful to even medium size enterprises, unless anyone can get public access to PBs of data, but that doesn't mean big data is dead. ChatGPT is literally changing how school will test their students, for a start. Ma…
> ChatGPT is literally changing how school will test their students, for a start.
Here's a novel idea: test students using pen and paper?
Re: Big data is dead
#87This is an excellent summary, but it glosses over part of the problem (perhaps because the author has an obvious, and often quite good solution, namely DuckDB).
The implicit problem is that even if the dataset fits in memory, the software processing that data often uses more RAM than the machine has. And unlike using too much CPU, which just slows you down, using too much memory means your process is either dead or so slow it may as well be. It's _really easy_ to use way too much memory with e.g. Pandas. And there's three ways to approach this:
* As mentioned in the article, throw more money at the problem with cloud VMs. This gets expensive at scale, and can be a pain, and (unless you pursue the next two solutions) is in some sense a workaround.
* Better data processing tools: Use a smart enough tool that it can use efficient query planning and streaming algorithms to limit data usage. There's DuckDB, obviously, and Polars; here's a writeup I did showing how Polars uses much less memory than Pandas for the same query: https://pythonspeed.com/articles/polars-memory-pandas/
* Better visibility/observability: Make it easier to actually see where memory usage is coming from, so that the problems can be fixed. It's often very difficult to get good visibility here, partially because the tooling for performance and memory is often biased towards web apps, that have different requirements than data processing. In particular, the bottleneck is _peak_ memory, which requires a particular kind of memory profiling.
In the Python world, relevant memory profilers are pretty new. The most popular open source one at this point is Memray (https://bloomberg.github.io/memray/), but I also maintain Fil (https://pythonspeed.com/fil/). Both can give you visibility into sources of memory usage that was previous painfully difficult to get. On the commercial side, I'm working on https://sciagraph.com, which does memory and also performance profiling for Python data processing applications, and is designed to support running in development but also in production.
Re: Big data is dead
#88Earlier quoted context omitted.
I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs. I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick so…
"Data launderer" would be a good job title...
That would imply the data is clean when they are finished. GIGO
Re: Big data is dead
#89Earlier quoted context omitted.
This would match what psychologists say about humans in general: we feel first, then we use our brain to justify that feeling. We’re not rational beings.
We totally are, it's just that rationality is a tool, not a guide. If you want to work out the truth, rationality will help you do that, but if instead you want to justify a decision you already made, well, it'll help you do that too.
Hypothesis don't come from rationality either, they result from well informed intuition. All of the formality of science is about tricking ourselves into discovering our intuition is wrong using a rational series of steps even when everything in our nature is to use that ability to reason to do the opposite.
Re: Big data is dead
#90"For more than a decade now, the fact that people have a hard time gaining actionable insights from their data has been blamed on its size." The real issue is that business people usually ignore what the data says. Wading through data takes a huge amount of thought, which is in short supply. Data Scientists are commonly disregarded by VPs in large corporations, despite the claims about being "data driven". Most corpo…
I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs. I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick so…
> I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs.
So, a synonym for 'consultant?' :)