collection VS cube.js

I took the official dataset () from MoMA and visualized the artists who have at least one drawing in the museum's collection and lived in 20 century.

The X axis represents the year of birth, Y — lifespan, size — count of works, color — gender.

() https://github.com/MuseumofModernArt/collection/tree/master

Portico takes published museum collection data and turns it into a virtual gallery/viewing room dynamically. This example is the first few items from the published MOMA collection ((https://github.com/MuseumofModernArt/collection).

The collection includes two datasets - ‘Artist’ and ‘Artwork’ available in both CSV and JSON formats. The data can either be forked or downloaded directly from the GitHub page. However, the dataset has incomplete information and should only be used for research purposes. That is why it’s the perfect candidate as it resembles a real-world scenario where data is often missing.

On GitHub, MoMA publishes and periodically updates a public dataset which contains ~140,000 records, representing all of the works that have been accessioned into MoMA’s collection and cataloged in our database. It includes basic metadata for each work (e.g., title, artist, date made, medium, dimensions, and date of acquisition). This dataset is placed in the public domain using a CC0 License (so we're free to use it in this tutorial) and available in CSV and JSON formats.

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: