Diving into the Deep End: A Python Journey in Water Management
summary
A SaaS platform springs from a simple spreadsheet into production software that does water accounting, delivery tracking, scheduling, reporting and simulation, built with Python + Django + HTMX, GIS visual layers and a custom graph model that represents canals as a directed acyclic graph. A sole developer grows the schema from about ten tables to 400 models, accumulates a 24 GB irrigation library, publishes packages, abandons microservices for a monolith, and runs successfully for real irrigation districts.
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chapters (15)
- 0:00Introduction
- 3:13Spreadsheets fail for water
- 6:21Python and Django over PHP
- 8:33Naive models to 400-table schema
- 9:36Learning graph theory and GIS
- 11:47Avoiding microservices and SPA traps
- 14:15Boring Django templates work
- 16:04Personal challenges beyond coding
- 18:34ADHD and time estimation
- 20:41Balancing two jobs and family
- 21:46Tooling and readability best practices
- 22:49Innovate by building
- 23:53Evolution to modern system
- 24:59Unexpected outcomes and impact
- 26:03Key takeaways
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