Ask HN: Data engineers, What suck when working on exploratory data-related task?

Great question. As a founder also working on my next project, the fear of "solving fake problems" is something I think about every day. Thanks for asking it.

For me, the single most frustrating part of any data-related task isn't the data itself. It's the "work about the work" – the soul-crushing feeling that I'm doing the same thing two or three times in different windows.

The biggest irony is that this is often caused by the very "smart work" tools that are supposed to make us more productive.

My typical workflow looks like this:

A request for data comes in on Slack. I pull the data, analyze it, and share a conclusion in the Slack thread. Then, I have to go to Jira to create a ticket that summarizes what I just said on Slack. Finally, I have to open Notion to write a brief document explaining the findings for the record. The context is constantly being copied, pasted, and fragmented. It's exhausting and feels like a waste of human potential.

This isn't a pitch, but this exact frustration is the only thing I'm focused on solving right now. My entire thesis is that the endless context switching between our communication layer (chat) and our execution layer (tasks, docs) is the biggest source of "fake work" in modern companies.

I'm building a tool where that entire "copy/paste the context" cycle is eliminated. A place where the conversation is the task, is the doc, is the context—all in one single flow.

I'm just a founder who is sincerely obsessed with this problem, and it's validating to see I'm not the one who feels this pain.

Not a data engineer but my work revolves around processing a ton of data(let's call it partial data engineering). Much of the data I get is inputted by humans from different sources and platforms and countries. My biggest pain in a nutshell - the human factor. Believe it or not, people have managed to misspell "Austria" over 11,000 times(accents, spaces, different encodings, alphabets, languages, null characters and so on. Multiply that by 250-something countries and multiply that by around 90-100 other fields which suffer from similar issues and multiply that by 2.something billion rows and you get the picture.

The main issues for problems like this fall into 3 categories

- Things that prevent you from starting the job. Org silos, security, and permissions

- Things that prevent you from doing the job. This is primarily data cleaning.

- Things that make the job more difficult. This involves poor tooling, and you'll struggle to break the stranglehold that SQL and python-pandas have in this area. I'll also add plotting libraries to this. Many of them suck in a seemingly unavoidable way.

On the second and third points llms will most likely own these soon enough, though maybe there's room to build something small and local that's more efficient if the scope of the agent is reduced?

The first point is organizational generally, and it's very difficult to solve outside of integrating your system into an environment which is the strategy pursued by companies like snowflake and databricks.

As clejack said, "Org silos, security, and permissions" - this is usually the largest single time sink on any project that needs production data.

Related to this is obtaining data in bulk - teams (understandably) are usually not willing to hand out direct read access to their databases and would prefer you use their API, and they've usually built APIs intended for accessing single records at a relatively slow rate. It often takes some convincing (DoSing their API) to get a more appropriate bulk solution.

Have been working on this for a while with real stakes.

You have two issues that computers cannot help with (by their nature). And this incidental complexity dominates all the rest.

1. What people want to do with data

2. Bureaucracies are willfully oblivious to this problem domain

What people actually want to do with data: Answer questions that are interesting to them. It is all about the problem domain and its geometry.

Problem: You can only falsify hypothesis when asking reality questions. Everything else will bankrupt you. You can only work with the data that you have. Collecting data will always be hard. Computers are only involved, because they happen to be good with crunching numbers.

Bureaucracies only care about process and never about outcomes. And LLMs can now produce random plausible PowerPoint material to satisfy this demand. Only plausibility ever mattered, because it is empirically sufficient as an excuse for CYA.

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Naval Ravikant (abridged): "Tell truth, don't waste word."

Mostly human problems especially if you work with Analytic teams. I need a PO for data. We usually don't have dedicated PO for data products so we have to do all the requirement findings by ourselves.

For exploratory data-related tasks, these are mostly related to checking data format or malformed data, so it is not a huge issue. But since you are building a product, I'll share my experience -> What I need is a quick way to explore schema changes in a column of a database table (not the schema of the table). Imagine you have a table `user` which has a column says `context` which is a bunch of JSON payload, I need to quick way to summarize and give me all "variations" of the schema of that field.

VisiData makes a lot of things easier.

Conversations and interviews > Jupyter notebook