Small teaser of TeeBI. This is the custom web server calculating a query and returning a chart. The only dependencies are TeeChart and Indy http web server component. Data is in column-oriented, memory-based custom format for speed reasons, no database or FireDac, etc are necessary. Data is imported from many sources (excel, text files, firedac or any other datasets, etc) to the custom format, which is then transparent to the user apps. The web server is optional if data is not remote, and it can also return binary streams to normal desktop (vcl or fmx), or mobile apps (fmx). A table of 2000x5 cells takes 180msec via web. For visual display, an fmx or vcl dbgrid easily handles 1 or more million rows with a custom "TBIDataset" linked to an in-memory data, allowing grouping, filtering and sorting using complex expressions involving columns from the same table or foreign-key(s) fields. This speed is the basis for a next-coming set of machine-learning and data-mining algorithms, currently under development.

Small teaser of TeeBI. This is the custom web server calculating a query and returning a chart. The only dependencies are TeeChart and Indy http web server component. Data is in column-oriented, memory-based custom format for speed reasons, no database or FireDac, etc are necessary. Data is imported from many sources (excel, text files, firedac or any other datasets, etc) to the custom format, which is then transparent to the user apps. The web server is optional if data is not remote, and it can also return binary streams to normal desktop (vcl or fmx), or mobile apps (fmx). A table of 2000x5 cells takes 180msec via web. For visual display, an fmx or vcl dbgrid easily handles 1 or more million rows with a custom "TBIDataset" linked to an in-memory data, allowing grouping, filtering and sorting using complex expressions involving columns from the same table or foreign-key(s) fields.  This speed is the basis for a next-coming set of machine-learning and data-mining algorithms, currently under development.