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· 4 min read

SQL Frames has several goals in terms of how it is distributed. As a library that can run within the browser or in the backend using Node or within an electronjs app. In addition, it should also be delivered as esm module or umd and cjs modules. Initially much of this bundling was managed with Rollup. In the initial stages when the entire codebase was in pure JavaScript the build time was around 2 seconds. Moving to TypeScript increased it to around 9 to 10 seconds when targeted to ESNext and almost double when targeted to older ES standards. This was simply killing productivity which was unacceptable. Yarn workspaces and esbuild is how this has been solved.

· 3 min read

SQL Frames has several types of DataFrames that neatly fit into a class hierarchy as expressed in TypeScript. One of the goals of SQL Frames, which makes it different from current Python Pandas, is an integrated user interface to all the different DataFrames. There are two main reasons for this.

· 3 min read

I will be posting a series of Thank you <techstack-component> posts to thank some of the important technologies used in building SQL Frames starting with this one about TypeScript.

Building a major framework with the goal to make it easy to use, by as many as possible and to solve as many use cases as possible, the choice of the programming language can potentially make a huge difference. SQL Frames gladly embraced TypeScript. As this was done mid-way after several thousands of lines of code, it was not a trivial effort. However, it did pay off well.

· 5 min read

My son got selected into ACSL finals which happened on 29th May. During the year he managed to get 40 on 40 which I think is awesome. He gave his best for finals as well but didn't manage to get any of the medals. While he was a bit disappointed, I personally think he did great. But is it me the dad saying this or is there some objectivity to this thinking? The good thing is ACSL finals data is publicly available and that immediately got me into thinking about doing some analysis. Here are my findings for the 2020-2021 Junior division.

· 3 min read

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.