Everyone who studies data structures and algorithms would come across tree data structure and related algorithms at some point or the other. The tree traversal algorithms provide the ability to do bottom-up aggregation, top-down propagation, breadth first, depth first processing and so on. While SQL provides a standard to process recursive data using the WITH RECURSIVE CTE clause, doing any complex analysis on recursive data using SQL is like using assembly language to build a web page.

As much as SQL Frames is a fan of SQL, this is one of the use cases where it has to breakaway from the limitations of it.

Drilldowns are an important UX element of any reports and dashboards. SQL Frames provides several charting features like multi-series, multi-axis, multi-lod (levels of detail), static and dynamic grid charts. However, the ability to drilldown has been missing. But today, I am pleased to announce that has changed with the introduction of the drillpath API to SQL Frames, a low-code data, visualization and intelligence platform for the browser.

CKAN is a popular open source data management system. It is used by Data.Gov and other public data initiatives through out the world. These systems allow a way to collect and catalog the data in various formats including CSV, one of the most common data formats. CKAN has extensions to provide data visualization. However, it appears that the visualizations are not widely deployed perhaps due to the additional server-side compute required to support them.

SQL Frames provides a client-side data, visualization and intelligence platform. It is ideal for working with remote data directly within the browser. Hence, we are making this technology available for previewing datasets from Data.gov.

Pivot tables and Sparkline charts have something in common. Both can show a column transformed view of data. Sparklines have the additional advantage of using less space. However, they also have the disadvantage that they only show the outline of the data due to smaller space. When done incorrectly, sparklines lead to incorrect decision making and bad user experience. This article provides the details of how SQL Frames implemented Sparkline charts to take care of both correctness and performance.

The two most popular charts are Top N charts and Trend charts. That is because almost all businesses want to ask questions like

1. Who are my top buyers (top N)
2. Which are the top selling product categories (top N)
3. Is the services revenue growing quarter over quarter (trend)
4. How did this stock perform in the last two years (trend)

There is also a third type of analysis that is applicable to certain use cases such as infrastructure monitoring, stock price fluctuations and monitoring of manufacturing processes. This class of analytics is near-realtime in nature.

SQL Frames can support such near-realtime analytics use cases. More importantly such user experiences can be easily tailored with SQL Frames low code API as shown in the demo below.