SQLpage VS cube.js

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> SQLPage is a tool that allows you to build websites using nothing more than SQL queries. You write simple text files containing SQL queries, SQLPage runs them on your database, and renders the results as a website.

The 22-line "TinyTweeter" example at 28:45 [0] in the video is a good overview - perhaps better than anything currently on the homepage/docs: https://github.com/lovasoa/SQLpage/blob/main/examples/tiny_t...

Also, based on a couple of discussions [1][2] it seems like SQLPage has the potential to combine well with HTMX too. The two projects definitely share a similar philosophy.

[0] https://youtu.be/mXdgmSdaXkg?t=1721

[1] https://github.com/lovasoa/SQLpage/issues/84#issuecomment-19...

[2] https://github.com/lovasoa/SQLpage/pull/175#issuecomment-187...

I am currently looking for a solution to run automated tests on a sql website generator I am working on ( https://sql.ophir.dev )

I wanted to use hurl (https://hurl.dev/), but Bruno's UI seems to be useful while developing the tests... Has someone tried both ? Which is better for automated testing, including when the response type is html and not json?

Full fledged BI tools like Superset and Metabase are amazing for their intended use cases.

But they may be an overkill if your primary use case is to infrequently build semi-interactive reports for non-technical end-users and your use cases are are mostly covered by standard graphs & tables. Esp. so if you are familiar with SQL and have access to the underlying data source. Two nifty utilities I have found to be very useful for latter kind of use cases are SQLPage and Evidence.

They make it very convenient to whip out some SQL and convert that to a neat professional looking web ui that can be forwarded to an end user. In case of Evidence it is a statically generated site, and in case of SQLPage it is a web app that connects to a live database.

SQLPage: https://sql.ophir.dev/

Evidence: https://evidence.dev

I feel obligated to add a shameless plug here. The idea is very close to a project I presented at pgconf.eu last week: SQLPage

https://sql.ophir.dev/

SQLPage has the same goal as postgrest+htmx, but is a little bit higher level. It let's you build your application using prepackaged components you can invoke directly from SQL, without having to write any HTML, CSS, or JS.

It would be great if someone could open a github issue with reproduction steps and maybe a screenshot: https://github.com/lovasoa/SQLpage/issues

The worst I'm able to get when manually disabling the cache and simulating a slow 3G connection is this: a blank page first, then text in the browser's font, then the text re-renders with the right font, then the icons load. The user should never see completely unstyled content.

The site uses "font-display: fallback" so this happens only on slow network connections. If the font loads fast enough, then the fallback never appears.

The official website for SQLPage (https://sql.ophir.dev/) is written in SQLPage.

The source code is here: https://github.com/lovasoa/SQLpage/tree/main/examples/offici...

The site also links to this little collaborative game written in SQLPage: https://conundrum.ophir.dev/

The github README has code snippets and associated screenshots: https://github.com/lovasoa/SQLpage#examples

There is also an official repl.it that you can fork to quickly try it online without having to download anything: https://replit.com/@pimaj62145/SQLPage

And SQLPage cloud is coming: https://sql.ophir.dev/your-first-sql-website/hosted.sql

Hey HN community!

I'll be making my first ever presentation at a large tech conference at pgconf.eu this December, where I'll be presenting the SQLPage webapp micro-framework ( https://sql.ophir.dev/ ). I'm eager to make a lasting impression and deliver a presentation that truly resonates with the audience at the conference, who probably knows more about postgres than I do.

That's where I could use your insights. What makes a good tech talk in your eyes? Do you like seeing mind-blowing demos, deep dives into code, compelling storytelling, or something else entirely ?

If you have any specific advice, tips, or ideas for structuring a tech conference presentation, I'm all ears. I want to ensure that my presentation is not just informative but also an experience to remember.

Thank you in advance for your guidance and suggestion !

When I see that, I always wonder whether this is part of the business plan of the people who distribute open source software for free, with a paid hosted version. There is some kind of a conflict of interest: the easier the software is to install and operate, the less attractive the hosted version.

I am working on an open-source software with a hosted version myself ( https://sql.ophir.dev ). It's a website builder, and I'm trying to make ease of deployment and operations a competitive advantage, which is marketed on the home page. But it may be idealistic to ask the same of others. My audience is mostly people who will have to operate the software themselves, whereas in most other domains, the people making the choice to use the software and the people who will then have to operate it are not the same.

I should have clarified. There's a large number of apps that are:

1. taking info strictly from SQL (e.g. information_schema, query history)

2. taking a user input / question

3. writing SQL to answer that question

An app like this is what I call "text-to-sql". Totally agree a better system would pull in additional documentation (which is what we're doing), but I'd no longer consider it "text-to-sql". In our case, we're not even directly writing SQL, but rather generating semantic layer queries (i.e. https://cube.dev/).

I'm not too familiar with https://cube.dev/ - but my initial impression is they are focused more on providing APIs backed by SQL. They have a SQL API that emulates the PostgreSQL wire protocol, whereas Spice implements Arrow and Flight SQL natively. Their pre-aggregations are a similar concept to Spice's data accelerators. It also looks like they have their own query language, whereas Spice is native SQL as well.

Hey HN!

Over the past year, my co-founder David and I have been building Delphi to let developers create amazing customer-facing AI experiences on top of their data. We're excited to share it with you.

David and I have spent our careers leading data and engineering teams. After ChatGPT got popular, we saw a rush of "chat with your data" startups launch. Most of these are "text-to-SQL" and use an LLM like GPT-4 to generate SQL queries that run directly against a data warehouse or database.

However, the general perception now is most of them make for nice demos but are hard to make work in the real world. The reason is data complexity. Even smart LLMs find it difficult to reason about messy databases with hundreds of tables, thousands of columns, and complex schemas that have been built up piece-meal for years. Text-to-SQL can be a fine dev tool for data scientists and analysts, but we've seen many organizations hesitate to deploy it to end users, who never know if the answer they get one day will be the same the next.

David and I found a better way. From our time in the data engineering world, we were familiar with a type of tool called "semantic layers." Think of them like an ORM for analytics. Basically, they sit between databases (or data warehouses) and data consumers (data viz tools like Tableau or APIs) and map real-world concepts (entities like "customers" and metrics like "sales") to database tables and calculations.

Semantic layers are often used for "embedded analytics" (e.g. when you're building customer-facing dashboards into your application) but are increasingly also used for traditional business intelligence. Cube (https://cube.dev) is a prominent example, and dbt has also recently released one. They're useful because with a semantic layer, the consumer doesn't have to think about questions like "how do we define revenue?" when running a query. They just get consistent, governed data definitions across their business.

We realized that semantic layers could be just as useful for LLMs as for humans. After all, LLMs are built on natural language, so a system that deterministically translates natural language concepts into code has obvious power when you're working with LLMs. With a semantic layer, we've found that companies can get AI to answer much more complex questions than without it.

For a year now, we've been building Delphi to do just that. We've gone through a few iterations/pivots (initially we were focused on building a Slack bot for internal analytics) and are now seeing our developer-first approach resonate. We're being used to power customer-facing fintech applications, recruiting software, and more.

How do you use Delphi? The first step is connecting your database; then, we build your semantic layer on top of it. Right now we do this manually, but we're moving more and more of it over to AI. Once that's done, we have 3 main ways of using Delphi: 1) white-labeling our AI analytics platform and providing it to your customers; 2) a streaming REST API and SDKs; and 3) React components to easily drop a "chat with your data" experience into your app.

If this is interesting to you, drop us a line at [email protected] or sign up at our website (https://delphihq.com) to get in touch. Thanks for reading! Would love to hear any thoughts and feedback.

We use https://cube.dev/ as intermediate layer between data warehouse database and Superset (and other "terminal" apps for BI like report generators). You define your schema (metrics, dimensions, joins, calculated metrics etc) in cube and then access them by any tool that can connect to SQL db

also check out cube.dev. they can do the semantic layer and cache it so you are not hitting Snowflake all the time.

We need metrics to live in source control with reviews. Metabase doesn't have a git integration for metrics, which is why we are convinced to use cube.dev as a semantic layer.

https://github.com/cube-js/cube comes out very well at the end as a promising open source system, getting rather close to the bullseye. Would love to know more & hear people's experience with it.

How is this different from something like https://cube.dev/

Have a look to cube.js

For now, I've been thinking on using self-hosted Rudderstack both for ingestion and reverse ETL, cube.dev as the abstraction later for building webapps and providing catching for the BI layer, and dbt for transformations. But I have doubts with the following elements: