evidence VS cube.js

We use echarts at https://evidence.dev and have been quite happy with it. We do a lot of embedded analytics and it's worked well for us.

It’s interesting to me how far you have pushed the SQL language in this framework, such that it truly is “SQL only”.

The challenge as I see it with enabling analysts to build websites is that you need to build abstractions to get from familiar (SQL, yaml) - the language of analytics, to new (HTML, CSS, JS) - the language of the web browser

As one of the maintainers of Evidence (https://evidence.dev), one of the things I’ve often considered is how accessible our syntax is to analysts. Our syntax combines SQL and Markdown, with MDX style components e.g.

The are inherently webdev-ey, and I do think they put off potential users.

On the flip-side, by adhering to web standards, you get extensibility out of the box, and working out what to do is just a Google search away.

Anyway, thanks for the thought provoking piece.

Dataclips was my first experiences writing SQL.

Writing code was a markedly better DX that building dashboards in Tableau, which is why I'm now working on https://evidence.dev - a SSG for creating data from SQL and markdown

Previous HN discussions:

I'm one of the founders of Evidence (https://evidence.dev) - would be great to hear about your experience. Reaching out now!

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

We used ECharts to build our charting library at Evidence and it’s been a great experience overall (https://evidence.dev).

We started with D3 and a few other tools, but felt that we get a lot more out of the box with ECharts, like interactivity and an events API. ECharts is also a lot more extensible than people give it credit for.

If anyone is curious, we documented the process of selecting a charting library after assessing several options: https://github.com/evidence-dev/evidence/issues/136

The new direction seems very similar to what evidence has been doing for a while

https://evidence.dev

I'm quite excited about this, and would also love to have it distributed as an NPM package.

I work on an OSS web framework for reporting/ decision support applications (https://github.com/evidence-dev/evidence), and we use WASM duckDB as our query engine. Several folks have asked for PRQL support, and this looks like it could be a pretty seamless way to add it.

> Not sure the language you choose matters as much as making the API usable by a wide audience.

Fully agree with this, and having typeset my masters thesis and later my resume using LaTeX, I think that the “authoring experience” is 100% the place to focus on improving.

If you’re interested in the “markup to document publishing” space, you might also be interested in the open-source report publishing tool I’m now working on, Evidence (https://github.com/evidence-dev/evidence)

It’s similarly based on markdown, though uses code fences to execute code, HTML style tags for charts and components, and {…} for JavaScript, i.e.

---

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: