How SmugMug Increased Data Modeling Productivity with Amazon Q Developer
DevOps & Developer Productivity Blog
This article details how SmugMug leveraged Amazon Q Developer to dramatically improve their data modeling productivity across their photo platforms SmugMug and Flickr.
- SmugMug uses Amazon Redshift, S3, Aurora, and DynamoDB to analyze petabyte-scale data
- Amazon Q Developer helped streamline their four-step data modeling process
- Key capabilities included:
- Generating SQL join statements
- Creating entity-relationship diagrams
- Translating code between languages
- Writing test queries
- Generating predictive machine learning models
- Productivity improvements included reducing data modeling tasks from days to hours
- The team plans to expand Amazon Q Developer usage across more complex data schemas and ML tasks
By adopting Amazon Q Developer, SmugMug increased data science and engineering team productivity by up to 100%.
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