DjangoCon 2022 | Predict Lightning Strikes using Django and AWS

Django Python

"Learn how a team built a cloud-native solution using Django, Python, and AWS to predict lightning strikes and improve safety, with features like Redis caching, GeoJSON processing, and containerized development."

Key takeaways

The project leverages Redis to cache the delivery of results from Nixrad data, RAP data, and other data sources.
The goal is to achieve a response time of under 500 milliseconds for subscribers.
The team uses GeoJSON to process, store, and transfer data between the application and users.
Container images for Lambdas are used to run the application locally, allowing developers to test changes quickly.
The project uses Amazon Web Services (AWS) and its services, including Lambda, ElastiCache, and Fargate, to build a cloud-native solution.
The project also uses Python, Django, and scientific computing tools like scipy and numpy to make predictions.
The team uses AWS’s NEXRAD data, and ingests around 450 gigabytes of data per day.
Predictions are made using algorithms that show where lightning will strike, helping to improve safety and reduce delays.
The team uses CodeArtifact and Pip to manage dependencies and build processes.
The project uses Terraform to automate infrastructure deployment and management.
The team uses environment variables and caching to improve performance and reduce latency.
The project uses AWS services like Route 53, SNS, and SES to manage DNS, events, and email notifications.
The team is working on expanding the platform to predict other weather-related events, such as tornadoes.

DjangoCon 2022 | Predict Lightning Strikes using Django and AWS

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