[Blog Proposal] 7 Lessons from Teaching Cloud-Native Geospatial This Year #98
Description
@cloudnativegeo/cng-editorial-board will review this submission.
Blog Post Title
7 Lessons from Teaching Cloud-Native Geospatial This Year (will likely adjust but a starting point)
Author(s)
Matt Forrest, Wherobots
Summary
This is a summary of observations that I have been collecting notes on from various presentations, videos, collaborative sessions, and discussions this past year summarizing the most important observations in talking about and advocating for CNG. This is a high level lest of the main points:
- "Cloud" can be a tricky term - for many it means that they too must use the cloud but that isn't always and often not the case
- There is a difference between those creating cloud native geospatial data and those using it, and both require some common ground but different lessons
- We need to do some more enablement around "cloud basics" - using cloud storage, what it is, why it's valuable - for many its an empowering moment moving your first data into a bucket
- Keep talking about the different engines that can read this type of data and helping users understand how they fit and what the differences are
- We need to bring this knowledge, specifically using the data, to other practice areas like data science and machine learning as they already use many of the same tools
- Having clear certifications/badges around these skills, both as users, builders, and architects, will be important for those individuals and organizations
- We are at a point where we need to clarify and specialize roles - the general Geospatial Specialist is asked to do too much - how can we define the roles and functions of Spatial Data Scientists, Data Engineers, Cloud Architects, Machine Learning Engineers, Analytics Engineers, and Developers?
Why this post is relevant to Cloud Native Geo
These are my observations from several different talks, conferences, and meetings. There is great interest in many areas from traditional GIS analysts to machine learning focused data scientists. I want to bring some ideas forward on how best to do that to advance CNG into different areas.
Timeline
When do you plan to submit your draft, and when would you like the post published?
Please note:
- The review process may take up to 2 weeks.
- The final draft must be submitted at least 3 days before your desired publication date.
- To ensure timely publication, please plan to submit your initial draft at least 2 weeks and 3 days before your intended publication date.
In the next 2 weeks is my plan!
- Draft submission date:
- Final publication date:
Anything else to share?
Do you have any drafts, resources, inspiration, or graphics to include with your post?
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