AquaYield

Inspiration

We wanted to build a product as if it were going to be sold as part of business operations.

What it does

We estimate the ocean toxicity to determine the best places the fish. This is useful for casual fishers who want more lively fishing areas, as well as aquaculture centers which want to protect their fish from dying.

How we built it

We scripted raw downloads into an S3 bucket. This was then processed in Databricks and moved to another S3 bucket.

Challenges we ran into

The scope to deliver was about right, but we lost some time trying to automatically download and process larger files which we couldn't handle well without more time.

Accomplishments that we're proud of

Building a job in Databricks was fun. It took a few hours but it was intuitive and simple, it just took a bit of adjusting.

What we learned

We learned a bit about how Databricks works and how to better set up infrastructure to avoid compute limits for future use. A lot of the features were nice, it was just slow to process a lot of items.

What's next for AquaYield

Finishing a shrub pipeline for wildfires.

Built With

• amazon-web-services
• databricks
• django
• javascript
• lambda
• numpy
• pandas
• pyspark
• python
• react
• s3
• sql
• typescript