Can we replace a $150k Looker with a bucket of HTML files?

Vaushite

AI wrote this

ekreloff

It would be been a more thorough analysis if they had accounted for the engineering teams’ salaries in time devoted to recreating this tool and maintaining it, rather than just tokens. In the end, it would likely work out to more like 50% cost savings. Still good, but certainly more expensive than this article ultimately claims

doctorpangloss

> We already run everything on Kubernetes, so deploying a service was mostly a matter of adding a Terraform module and a Helm chart. Haha "we run everything on kubernetes, so deployment is a matter of using the least logical complement to it, and the lowest budget, flakiest and overbuilt alternative to plan manifests with none of the benefits of an operator"

physhster

ERR_CONNECTION_REFUSED I guess I'm not reading this...

cachius

What is Looker?

vessenes

Enterprise friends: what is snowflake and why did it have fine grained permissions for a whole company? I didn’t understand it from the snowflake website.

alexkbuilds

I built something similar in my previous job. The part that we were never able to figure out is how to guarantee that everybody is looking at the right numbers. Even if you have a comprehensive semantic layer, there are still a lot of things that can go wrong in putting metrics on a dashboard. This is why analysts are still needed, IMHO.

iamacyborg

At least replace it with dbt charts rather than a whole new vibe coded mess.

phone_book

Over a decade ago i worked at a company with a BI tool that was exactly this, minus the AI of course. it was just PHP pages backed by embedded SQL built by a non engineer. anytime someone wanted a different slice of data, the person just copied an existing page and swapped out the sql and the column names. URL query string was used for the different parameters of the SQL statement, i forget if it was open to sql injection but it was an internal tool only. even after implementing a BI tool everyone still used the PHP pages because it was faster and easier to just bookmark the specific page you want with the data you wanted. much like low code tools, most people dont want to mess with self serve BI. AI might help with that but i think the tool in the article opens the door for people creating slightly different versions of the same thing. also, claude is awful at visualizations. every one i see looks the same, crammed full of bad charts and too much text, poor color choices, and no real value in terms of story. still, this is an interesting implementation of that concept.

miradu

if you want to operate this 'bucket of HTML files' over time, my startup https://valet.dev gives you the tools to host, govern, enforce policies, and secure what vibe coders create at your company. The world of who can create software is growing (hello citizen developers!) but the challenges of operating and maintaining software is just as hard.

Glyptodon

BI tools have always been borderline easy to replace IMO. So long as you have a decent data layer.

fifilura

Were the dashboard used for insights or for making the managers feel good?

unusualmonkey

We just exited looker by having codex migrate the dashboards to html. Now it's just a git repo.

jamukakar

Building is easy, supporting and sustaining is harder especially in light of things like all the CVEs. My startup valet.dev is a platform for skills, websites, apps, and agents that is solving for this by baking governance and sustainability into the system. All engineering teams need this, but as we see the rise of citizen developers bring productivity gains to their companies the need for collaboration, security, and sustainability builtin to the environment they work in is even more important than ever.

bushido

> A BI tool is mostly a way to turn a query into a picture, and a picture now costs about $0.50 of tokens. It gets hard to justify a $150k licence for that part That is an awfully simplified view of BI. In my experience, most companies struggle to define what is important, what needs to be measured, and how those measurements should be used to improve business. While businesses try to work these details out, they try getting their analysts to in the author's words print out good pictures. But the BI tools really exist to make it easy for analysts to navigate hairy internal situations. Producing a picture or HTML more easily is not going to solve the decisioning problem. If anything it might make it worse. In the end, it's going to cost a lot more than 50 cents a picture.

fidrelity

It reads to me like their first version (sharing the output html) essentially skipped their permission layer. That seems like a dangerous decision if someone can share the sales data analysis openly with users not allowed to see such data?

deepsquirrelnet

For a related use case, I have been using claude to add or change features in my own tensorboard log reader. The flexibility of being able to change anything at any time so I can see what I want to see is really nice. I've always preferred local monitoring tools, but tensorboard is seemingly just on life support at this point. https://github.com/dleemiller/BlernsBoard

dominotw

Billionaires out there are gaining billions everyday meanwhile i see common folk nickle and diming each other. " look how i vibecoded this app and save $1.99"

dvdhsu

I work at Retool, and probably the majority of our business today is "replacing SaaS with citizen-developed apps". I think that a lot of these CRUD-y SaaS products (which is, really, what BI is) will disappear over the next few years. The bottlenecks I see: * Data quality — we've seen Databricks / Snowflake schemas with 10^5 tables. Just putting a LLM on that doesn't work - you do need some sort of semantic layer. This is especially bad since LLMs are like slop cannons — if you ask it to prove X with the data, it will build you an app / visualization that proves X. * Deduplication — it turns out many people want moderately different views of the same data. How can you host everything centrally and DRY everything out such that you can guarantee correctness? * Writing data back — this is where it gets really hairy. Dashboards that only read data from Databricks is dangerous insofar that data can leak. But once you start needing to write data back, you need access to APIs / MCPs... and then you want some sort of authorization layer (since many SaaS apps don't have great authorization within themselves). Obviously we work on quite a bit of this at Retool, but we are always looking for ideas on how to make this better. We believe that many things should be in a platform (e.g. centralized data connectivity, authentication, authorization, etc.) setup by engineering, and then non-developers can go crazy generating safe apps.