Show HN: Open-source BI and analytics for engineers

Superset is a beautiful tool focused on self-serve with amazing visualizations. I won't take anything away from them!

Our thesis is that self-serve is much less important than people think, and we find people often make a mess of never-ending dashboards. Current BI tools struggle to prevent that. We solve this problem with a core of software engineering practices.

Hey, OP here. We love what dbt has done for transformation-layer engineering. But we often see companies still struggling with a mess of unstructured dashboards, even with solid dbt models underneath.

The problem is that dbt models and BI dashboards are often managed by separate teams. Quary brings the two together, letting engineers define reusable models and build well-structured dashboards on top of them in one cohesive, code-first environment.

Big fans of our fellow YC mates at Lightdash!

There are some core differences that make our product feel quite different:

- Lightdash isn't Lightdash without dbt so you always have that divide even though they have done a fab job of minimizing it.

- The editor for us is in Visual Studio Code which means you don't have that jump and can iterate all together.

- Every thing is version controlled as a file in your repository which means you can add those engineering practices to the dashboards/charts themselves.

We do if you use DuckDB and you pull data from your data sources through DuckDB. DuckDB can act as a single interface between multiple data source types. Feel free to DM me with any more questions. around your specific use-case and I can help.

Hey, Ben here from Quary; very valid comments like the one below copied meant we rethought our strategy it a little. We want to be open source but think we need a little protection.

"Hate to derail the conversation, but is Quary something I could easily whitelabel to embed BI into my product for my customers? (Passively) looking for solutions in that that don't feel dumbed down."

Hey! Thanks so much, really appreciate the feedback

thanks!

i tried org mode for sql queries but than went back to sql mode because lsp is not supported in org mode. Also how do you use charts with it?

Appreciate the kind feedback! Curious to know if org-mode is still actively maintained.

We're big fans of the Evidence team. While there's some overlap, we have a heavier focus on data modelling (similar to dbt). The key difference is we've rebuilt the modelling layer in Rust, leveraging WASM for better performance and browser-based execution. This lets us build a more seamless, end-to-end workflow encompassing transformation + viz optimised for the web.

Hitting us with the hard questions! And honestly this is something people overlook about the realities of the data space.

It's always easier to communicate what you are not, so let's begin there:

- drag and drop query builder - absolute prettiest graphs - tailored to the least sophisticated user

In addition to what I think we are not, I think there is some space for our belief about what the data space is/is not:

- We don't think data self-serve is possible but rather small datasets can be tailored. Fundamentally it comes down to complexity and data is complex. It takes expertise/skill to get value from data.

- It takes experts to get value from data, but as systems get better it will take fewer.

- Businesses should not be data driven, they should be reason driven.

- We don't think data dominates business, it's a supporting tool and so we don't think it will every the entry point for everything but rather supports process, so we want to appear in places where we can support that reasoning, like a chart in a Notion doc.

Now a few bits about what we are:

- Tool for those experts and engineers

- Tool to make them the most productive ever

- Prevent messes that people get themselves in in BI by injecting software engineering practices into the process (we know many companies with full time employees responsible for cleaning up messes)

Great to see BIRT mentioned on HN. I use BIRT to generate PDFs for clients. Modern BI tools are about interactivity and real time but PDFs still have a role in BI and BIRT does the job. As it uses JDBC to connect to data sources you can connect to most data sources. For many tools these days one of the first things you have to check is which data sources does it connect to. If you use a less popular database chances are your database will not be supported. I have worked in organisations that use DB2, Sybase, Oracle and so on and these tend not to be supported by modern BI tools. PDF generation also seems to be a snapshot of the page. So yes BIRT is a great tool, old school and a bit clunky but it does the job.

This is awesome! Great to see this project still alive after so many years :)

No sign in needed!

Hey, great question ... Again another tool we love. A few key differences:

- Visual studio code as the editor through and through

- Dashboards are fully defined in code Quary which is different to Rill

- At its core our architecture is also very different, Rill is built on top of Duckdb for that interactivity which can call out to other databases whereas we can call other SQL databases without everything going through DuckDB.

Appreciate the feedback! We'll keep this in mind.

There is nothing to host/provision, so it's simple in that sense. You just run it locally with your credentials and connect directly to your database.

It is definitely not the easiest to set up especially when thinking as a team so we'll keep that in mind.

Appreciate your feedback and guidance.

I was just at the Click-house office a few weeks ago - this is a really good idea.

Hey! OP here. This made my day, thank you!

Great! Feel free to reach out to me with any questions.

Hey! OP here. This is really good feedback thank you.

Someone else added this as an issue! We're happy to take a look at it. To gage interest please upvote it :)

https://github.com/quarylabs/quary/issues/4

The biggest difference I see (though I'm not super familiar with Plotly) is that we define data transformations in SQL, while Plotly uses Python. One benefit of SQL is that it provides the advantage of tracing data lineage from source to visualization, which gives you visibility into data dependencies - something that Python code in Plotly Dash doesn't offer.

That's useful feedback for us! We can improve our messaging.

The simple answer is yes: Our focus is code first, from modeling to charts and dashboards, and not self-serve.

We often found that keeping BI applications/dashboards organized is very difficult so we're adding engineering practices.

We've been focusing on the core VS Code extension and haven't released sharing yet. The plan is to provide a Vercel-like experience for deploying and sharing graphs.

People will be able to connect their GitHub repositories, deploy dashboards, and share them via our website. The interface will allow switching between branches and time-traveling between different states of the dashboard.

Here's a preview: https://www.youtube.com/watch?v=MD6In-iUd9g

Hey! We love Redash too. Where Quary is different is that we have more of an emphasis on Transformation. This means people can split out complex SQL blocks into modular, reusable components which improves data lineage (how the data flows from table to visualisation).

Dbt makes transformations modular and easier. It applies software development methods to the T of ELT.

Hey! We killed the auth flow from our extension, we used it to get an idea of how many people are using it. The extension works entirely local and connects to your database through your machine. So there's no need to self-host anything!

Ben here from Quary.

We love Grafana! It's fab for building dashboards, but it's focused on dashboarding/alerts and on pulling from various data sources, not just SQL.

Quary is purely focused on SQL, and crucially, it allows you to build up and develop more complex transformations.

Hey! OP here, I don't have a clear answer for this yet. We're exploring ways to make Quary more extensible. We are focusing on the core piece first, happy to chat to hear more about your specific use-case.

From what I can see, OpenDashboard is tackling workflow automation tasks. We're more focused on the data modelling process.