There is a lot of buzz around NLP based data analysis these days. But I think the good old pivot table UI still feels much faster to analyze data.

Comments URL: https://news.ycombinator.com/item?id=35781460

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Comments URL: https://news.ycombinator.com/item?id=34710247

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Comments URL: https://news.ycombinator.com/item?id=32182674

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How does this compare to react-virtualized (https://github.com/bvaughn/react-virtualized)? Does it handle tables (rendered with table elements and not div)? Does it support column virtual scrolling?

What level of sophisticated queries can be created with it? For example, can it do "Top N and Other"? Which BTW, was recently added for SQL Frames ( https://sqlframes.com/docs/data_sets/top_n_other_sets )

Most of these visual SQL builders I have seen only tackle the basic level of SQL. I am interested in an advanced SQL builder myself to plug as a no-code SQL builder to front the SQL Frames API.

I didn't find a way to edit my initial comment.

const employees = SQL.values([{ title: 'Developer', country: 'USA', salary: 120, payroll_tax: 20, healthcare_cost: 6 }]); employees.schemaName = 'employees'; const { groupBy, where: { gt, eq, and }, agg: { count, sum, avg } } = SQL; return employees.pdf(SQL.script('[salary]+[payroll_tax]').as('gross_salary'),SQL.script('[gross_salary]+[healthcare_cost]').as('gross_cost')) .fdf(and(gt('gross_cost',0),eq('country','USA'))) .gdf(groupBy('title','country') ,avg('salary').as('average_salary') ,sum('salary').as('sum_salary') ,avg('gross_salary').as('average_gross_salary') ,sum('gross_salary').as('sum_gross_salary') ,avg('gross_cost').as('average_gross_cost') ,sum('gross_cost').as('sum_gross_cost') ,count().as('count')) .having(gt('count',200)) .orderBy('sum_gross_cost');