More transparency, more granular and more ...
Follow up to prior post on working with data of different categories: https://www.aapg.org/care...net/activity/aft/610
As an example, here is a data app: https://shaleforce-aapg-p...APG_Majors_Portfolio
that brings to life, industry players mentioned in the Explorer September 2022 https://explorer.aapg.org...in-an-energy-crisis?
It illustrates what we shall strive for - zero downtime data to analysis, as a community.
Be that time series, production data and other data categories, the problem-solving execution challenge remains - how we connect data to app without moving a single byte. Different cloud technology players are working to crack that problem, and one stands out is Snowflake. https://github.com/Snowfl...for-python-streamlit
Observation - with the acquisition of Streamlit plus its new Python and Snowpark connector, it makes tying together data, analysis and cloud deployment doable without in-house heavy weight IT engineering team and vendor lockin.
Feel free to share you cloud data to analysis experience and lesson learned.