formica | Simple Tool to deploy Cloudformation Templates | AWS library
kandi X-RAY | formica Summary
kandi X-RAY | formica Summary
Dropped Python 2 compatibility, please upgrade to Python 3. The tool might work or it might not, its not tested against Python 2. Formica makes it easy to create and deploy CloudFormation stacks. It uses CloudFormation syntax with yaml and json support to define your templates. Any existing stack can be used directly, but formica also has built-in modularity so you can reuse and share CloudFormation stack components easily. This allows you to start from an existing stack but split it up into separate files easily. For dynamic elements in your templates Formica supports jinja2 as a templating engine. Jinja2 is widely used, for example in ansible configuration files.
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Top functions reviewed by kandi - BETA
- Update a Cloudformation stack
- Creates a new stack
- Removes existing ChangeSet from Cloudformation
- Create a change set and wait for it to finish
- Update a stack set
- Compare the template against the stack
- Generate a diff table
- Compares a Cloudformation stack set with the given template
- Add stack_set instances
- Parse the operation preferences
- List accounts
- Prints a text table
- Computes the diff between the given arguments
- Compare a CloudFormation stack
- Compare a stack set
- Describe the stack
- Create a function from a YAML node
- Returns the name of the deploy
- Create a new change set
- Construct a Resolver from a YAML node
- Decorator to handle artifacts
- Creates a function that waits for the given stack
- Load a template file
- List stacks
- Remove StackSet Instances
formica Key Features
formica Examples and Code Snippets
Community Discussions
Trending Discussions on formica
QUESTION
So I have two tables:
...ANSWER
Answered 2018-Jan-12 at 18:59Here's an example of how you might do that.
Create tables & a trigger:
QUESTION
Dataframe:
...ANSWER
Answered 2017-Nov-05 at 19:38A simple lapply
will do what you want.
QUESTION
before I start here is a a small subset of the data I'm working with, i apologize in advance for it being so large (note this is only the first 30 rows of an extremely large dataset:
...ANSWER
Answered 2017-Nov-05 at 17:43Simply use by
to slice a dataframe by needed factor(s) and run operations like vector return:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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