Timeline for Docker for data science
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 24, 2019 at 11:37 | history | edited | Dr. H. Lecter | CC BY-SA 4.0 |
edited body
|
| Aug 18, 2019 at 12:56 | vote | accept | nolwww | ||
| Aug 26, 2019 at 1:52 | |||||
| Aug 17, 2019 at 13:00 | vote | accept | nolwww | ||
| Aug 18, 2019 at 12:53 | |||||
| Aug 17, 2019 at 12:58 | comment | added | Dr. H. Lecter | No I meant that sandboxing is one of the many applications. Test environment is a bit larger because you want your environment to be as close as possible to the one you run your operations on and include sandboxing, but also bug fixing for exemple. For security, it relates to the devops culture in which you don't want to mess up with what is working (operations i.e. your database in the example you gave) and developments (trying new things). | |
| Aug 17, 2019 at 12:54 | comment | added | nolwww | So, in data science, Docker has nothing to do with Sandboxing? And the reason about security would better be "Having a test environment for the application"? | |
| Aug 17, 2019 at 12:40 | history | answered | Dr. H. Lecter | CC BY-SA 4.0 |