Welcome to SQL Frames
In a world that is increasingly generating and consuming data, it should be easy for anyone to tinker with the data they have access to and gain better insights. Yet, most existing data analytics tools are not easy to use, or require complex multi-tier technologies that are only understood by the tech savvy or simply too expensive. All of this causes the information divide, those that know how to use complex and/or expensive tools to extract value out of the data and those who don't.
SQL is a tried and trusted data analysis technology that has been there for nearly 50 yrs. Its success stems from its declarative nature. That is, you specify what you want to be computed rather than how it should be computed. However, SQL has been primarily for technical people.
Excel on the other hand made anyone who can write simple formulae to easily do data analysis. But doing anything more complex, people tend to move to other technologies like Python Pandas. Python Pandas is a great technology that introduced the DataFrame concept to the world. Essentially providing a set of APIs to load data from files or databases and do all kinds of in memory data transformations. However, those familiar with SQL can not readily use their knowledge to work with DataFrames.
What if you could compose your data transformation logic using DataFrames but declaratively using familiar SQL constructs? Introducing SQL Frames, a low-code technology that allows authoring DataFrames using SQL constructs. In addition, it is built ground up using TypeScript, allowing the entire logic to be executed within the browser.
Modern browsers have very fast JavaScript engines making it possible to perform complex data analysis tasks all within the browser. That is why, we see libraries like Tensorflow.js that are allowing us to execute complex machine learning algorithms within the web browser.
Even large organizations, when it comes to analyzing data deal with both small and big data. A company trying to do gender bias analysis needs just employee data and there are only a handful of companies that have more than a million employees. If you are a successful shopify store or an Amazon merchant, do you have more than a million transactions per year? The same sales data for Amazon can be considered to be big data while for the merchants selling on it, it is data that is manageable for analysis within their web browser!
SQL Frames is designed with the premise that there are several interesting use cases that can be solved by just loading the data within the browser. However, it is also designed to scale up for big data by automatically transpiling the composed DataFrame logic into SQL. This allows loading all the detailed data or fall back to querying the big data backend to get the right level of summarized data which further can be analyzed within the browser.
SQL Frames is designed to be extensible with plugins and by default it already has sophisticated UI and charting capabilities. Other tasks such as data wrangling, statistical analysis and full text search are also possible. Checkout the documentation for more details. All the examples in the documentation are executed live within your browser!
While the core SQL Frames technology is a low-code option reachable to developers, data scientists and data analysts, additional apps are being built on top of this solid foundation to make this technology reachable to no-code users.