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🎉Celebrating 100 Challenges! 🎉
It’s been nearly two incredible years, and we are ecstatic to announce that we’ve reached our 100th challenge milestone!
The Frosty_Friday team is amazed at the vibrant community that has blossomed around F_F. We’ve met some truly amazing people, and the support we’ve received from Snowflake has been phenomenal.
When we embarked on this journey, we never could have dreamed that Professor Frosty and The Intern would have their very own booth at the Summit!
To celebrate our 100th challenge in grand style, we reached out to the creative minds at Snowflake for some especially spicy challenge ideas. And let’s just say—they’ve delivered beyond our wildest expectations.
Among the many fantastic ideas we received, one particularly stood out. Dash, the Lead Developer Advocate at Snowflake, proposed a brilliant concept that combines the best of both worlds: old and new, with a splash of Streamlit added for extra flavor.
Here’s your task to join the celebration:
Using the startup code provided below, you’ll create a table with 2 columns: FILE_NAME and IMAGE_BYTES.
Startup Code
create or replace table images (file_name string, image_bytes string);
IMAGE_BYTES
Find the actual data right here in our S3 bucket : csv file
We can’t wait to see the creative solutions you’ll come up with. Let’s make this 100th challenge one to remember!
Happy coding and here’s to many more challenges together! 🥳
Remember if you want to participate:
- Sign up as a member of Frosty Friday. You can do this by clicking on the sidebar, and then going to ‘REGISTER‘ (note joining our mailing list does not give you a Frosty Friday account)
- Post your code to GitHub and make it publicly available (Check out our guide if you don’t know how to here)
- Post the URL in the comments of the challenge.
Excellent challenge! Took me out of my comfort zone, had to Google a bit.
Congratulations on the 100th challenge!
We believe that Frosty Friday has grown many Snowflake users.
Let’s take on the next 100 challenges together!
…And, it’s my simple solution, it works in the local Streamlit environment.
Loading the CSV into Snowflake first is a much better solution.
Parsing CSVs myself is a nightmare.
Notebooks would be a good option.
And, there are many ways to display images.
Congratulations on Week 100!
Thank you so much for always providing excellent problems.
This time, I used infer_schema() and the metadata_option of copy to first import the CSV into Snowflake.
After that, I used Streamlit in Snowflake, which I’m not very familiar with, to visualize the data! I selected the file name with a radio button, and the image was displayed below. It was a very cute image!
Week100おめでとうございます！素晴らしい問題をいつも本当にありがとうございます。今回は、infer_schemaとcopyのmetadata_optionを使い、まずSnowflakeにCSVを取り込みました。その後は、なれないStreamlit in Snowflakeを使い、可視化しました！ファイル名をラジオボタンで選択し、下に画像が表示する形で。非常に可愛い画像でした！
I was gutted when I didn’t have time to look at this last Friday! Had fun with this latest challenge, as I always do.
I had to actively prevent myself from overengineering the solution here as it’s tempted to go the whole distance here by getting the Streamlit to download the files from the S3 bucket during runtime, for the “live” experience and therefore skipping any preamble SQL. I avoided this though as I didn’t want to go through all the external access side as it felt like it was doing too much for this challenge.
Instead, I kept it nice and simple. Ingest the data, then get Python/Streamlit to decode and display it.
Bonus points to Frosty Friday for the cute images!
And one last point: A huge congratulations and thank you to the Frosty Friday team: Christopher, Mike and Sofia. Frosty Friday is a fantastic and fun way to stay current on Snowflake functionality. I can’t believe it’s been 100 weeks already, and that you’ve been involved in two Snowflake Summits! I hope to see the FrostyFriday booth return for the next Summit too, since it’s great fun and the booth does well at inspiring the wider community to get involved. Congrats and thanks!