awesome-aws | curated list of awesome Amazon Web Services | AWS library

 by   donnemartin Python Version: 0.1.0 License: Non-SPDX

kandi X-RAY | awesome-aws Summary

kandi X-RAY | awesome-aws Summary

awesome-aws is a Python library typically used in Institutions, Learning, Education, Cloud, AWS, DynamoDB applications. awesome-aws has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However awesome-aws has a Non-SPDX License. You can install using 'pip install awesome-aws' or download it from GitHub, PyPI.

A curated list of awesome AWS libraries, open source repos, guides, blogs, and other resources. Inspired by the awesome list.

            kandi-support Support

              awesome-aws has a medium active ecosystem.
              It has 11536 star(s) with 1612 fork(s). There are 491 watchers for this library.
              It had no major release in the last 12 months.
              There are 5 open issues and 14 have been closed. On average issues are closed in 73 days. There are 60 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of awesome-aws is 0.1.0

            kandi-Quality Quality

              awesome-aws has 0 bugs and 0 code smells.

            kandi-Security Security

              awesome-aws has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              awesome-aws code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              awesome-aws has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              awesome-aws releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              awesome-aws saves you 130 person hours of effort in developing the same functionality from scratch.
              It has 327 lines of code, 27 functions and 14 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed awesome-aws and discovered the below as its top functions. This is intended to give you an instant insight into awesome-aws implemented functionality, and help decide if they suit your requirements.
            • Rock the content of a readme file
            • Process a GitHub line
            • Computes the score of the given stars
            • Updates the line score for a given repo
            • Extract User ID from url
            • Write the result to a file
            • Print broken repos broken
            • Login to GitHub
            • Return the config file path
            Get all kandi verified functions for this library.

            awesome-aws Key Features

            No Key Features are available at this moment for awesome-aws.

            awesome-aws Examples and Code Snippets

            Pandas : Filter Data from one dataframe column and update another df column
            Pythondot img1Lines of Code : 8dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pat = '|'.join(r"\b{}\b".format(x) for x in brand_df['item'])
            #if dont need words boundaries
            #pat = '|'.join(brand_df['item'])
            item_df['updated_column'] = item_df['item'].str.extract('('+ pat + ')', expand=False)

            Community Discussions

            Trending Discussions on awesome-aws


            AWS codeBuild/codePipeline with serverless framework
            Asked 2018-Jul-20 at 07:58

            I am trying to automate Deployment Pipeline for my application. Here is the automation architecture, I came up with:

            As you can see, I am using codePipeline and codeBuild to automate my deployment. My backend is based on Serverless Framework, which deploys lambda functions on firing sls deploy command. This is the reason, I did not use codeDeploy to do traditional deployment. buildspec.yml file looks like this:



            Answered 2018-Jul-19 at 16:52

            Question 1


            Serverless framework now supports referencing variables from the Parameter Store. That means you can skip defining them in CodeBuild as serverless will retrieve them from Parameter Store upon deploy.




            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install awesome-aws

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            pip install awesome-aws

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