react-chartjs-2 VS cube.js

Chart JS is a great choice for creating charts as it has multiple visualizations. There are different integrations for the JS frameworks, and we shall use react-chart-js-2 library for Next JS.

React Chartjs 2 - Visit react-chartjs-2.js.org for more information. React Chartjs 2 is a React wrapper for Chart.js, a popular JavaScript charting library. It provides an easy way to integrate Chart.js into your React applications, allowing you to create a variety of charts and graphs with the underlying features of Chart.js.

Website: react-chartjs-2 GitHub page

If you’ve heard of Chart.js, you might wonder what React-chartjs-2 is all about. Well, think of it as a handy tool for using Chart.js within your React projects. Chart.js is a JavaScript library that helps create various types of interactive charts using HTML5 Canvas.

React Charts js

I'm currently using chart-js react-chartjs-2 it's ok but just wondering what else people could recommend for speed of implementation.

An example of a Snowflake API request using JavaScript, React, and Chart.js. This example uses react-chartjs-2, a wrapper for Chart.js. Install the dependencies:

React ChartJs 2 is a wrapper for the Chart.js library, which is a Javascript chart library used to build chart components with the HTML5 canvas element. In addition, Chart.js features can be used in React ChartJs 2. To get the most out of React ChartJs 2, you need to be familiar with Chart.js docs.

In the frontend, we decided to display the analytics data via a graph using the react-chartjs-2 library. React-chartjs-2 is a React wrapper for chartjs, a popular charting library for JavaScript. This library is simple, beginner friendly, maintainable, and flexible. It has a large community of users and thorough documentation. This provides for quick execution with the library. We also implemented the dashboard UI flexibly, so that any historical data point could be selected and displayed as requirements change in the future.

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: