About Beer Analytics
Beer Analytics is a database of beer brewing recipes, built specifically for data analysis. The database currently contains 1,002,311 recipes, mostly from the homebrewing community. It is made for beer enthusiasts and (home)brewers to provide detailed insights into brewing recipes, even when they're not an expert in data analysis. The goal is to expand the knowledge how certain types of beer are typically brewed, ultimately helping (home)brewers to compose better recipes themselves, and potentially uncover some trends in craft/home brewing.
It as created by beer enthusiast, homebrewer and software engineer Christian Scheb, who was feeling the need to get better insights how certain beers are composed, other than clicking through dozens of recipes. After starting a Jupyter data analysis project for himself in August 2020, he decided build a website from it, so other homebrewers can benefit from these insights alike.
I'm happy to hear your feedback about this page. Would you like to see a new chart/metric? Are you missing a feature? Feel free to contact me:
- Create an issue on GitHub
- Send me a Tweet
- Send me a message on Instagram
- Or send me an old-school email: mail (at) christianscheb (dot) de
Beer Analytics is a personal project. It doesn't generate any revenue for me, I bear its expenses on my own, including server infrastructure and operational maintenance. If you find this page valuable and would like to show your appreciation:
Thank you! :)
- Where are the recipes from?
- The recipes are gathered from various homebrewing communities online and historical archives. The database is regularly updated with new recipes on a daily basis. For details on the origins of these recipes, please refer to the "Brewing Recipes" sections across various pages.
- Can I have a copy of your recipe database?
- Thank you for your interest in our recipe collection! However, I am not able to redistribute the recipes. Each recipe in our database is the creative and intellectual property of its respective author, and it's important for us to respect their rights, as well as the usage rights of their communities. While I can't provide a direct copy of the recipe database, I encourage you to explore the "Brewing Recipes" sections on our platform. There you'll find links to the original sources of these recipes. This way, you can access them directly and also support the authors and communities by visiting their own websites or publications.
- How do you handle duplicates in the database?
- At present, the approach to handling duplicates is: we don't actively manage them. This decision was made after quality checks and analysis of our database. It showed that duplicates do not pose a significant issue, so time was better spent towards enhancing other features that more directly impact user experience and system performance.
- What technologies is it built with?
- Beer Analytics is using Python and Django for the backend. Data analysis is performed mostly with Pandas and results are visualized with the fantastic Plotly library. The frontend is built with SCSS and TypeScript for ease of development and is bundled with Webpack. The source code is available on GitHub, feel free to check it out!