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  1. Stackups
  2. DevOps
  3. Continuous Integration
  4. Continuous Integration
  5. AWS DataOps Development Kit vs GitHub Actions

AWS DataOps Development Kit vs GitHub Actions

OverviewComparisonAlternatives

Overview

GitHub Actions
GitHub Actions
Stacks48.2K
Followers3.1K
Votes27
AWS DataOps Development Kit
AWS DataOps Development Kit
Stacks1
Followers1
Votes0
GitHub Stars271
Forks25

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Detailed Comparison

GitHub Actions
GitHub Actions
AWS DataOps Development Kit
AWS DataOps Development Kit

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

It is an open source development framework for customers that build data workflows and modern data architecture on AWS. It offers high-level abstractions allowing you to build pipelines that manage data flows on AWS, driven by DevOps best practices.

Multiple workflow files support; Free and open source; Workflow run interface; Search for actions in GitHub Marketplace; Integrated with Github's Checks API; Logs and artifacts downloading support
Open source development framework; Build data workflows and modern data architecture; Extensible; Offers high-level abstractions
Statistics
GitHub Stars
-
GitHub Stars
271
GitHub Forks
-
GitHub Forks
25
Stacks
48.2K
Stacks
1
Followers
3.1K
Followers
1
Votes
27
Votes
0
Pros & Cons
Pros
  • 8
    Integration with GitHub
  • 5
    Free
  • 3
    Easy to duplicate a workflow
  • 3
    Ready actions in Marketplace
  • 2
    Read actions in Marketplace
Cons
  • 5
    Lacking [skip ci]
  • 4
    Lacking allow failure
  • 3
    Lacking job specific badges
  • 2
    No ssh login to servers
  • 1
    No manual launch
No community feedback yet
Integrations
GitHub
GitHub
Python
Python

What are some alternatives to GitHub Actions, AWS DataOps Development Kit?

Jenkins

Jenkins

In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.

Travis CI

Travis CI

Free for open source projects, our CI environment provides multiple runtimes (e.g. Node.js or PHP versions), data stores and so on. Because of this, hosting your project on travis-ci.com means you can effortlessly test your library or applications against multiple runtimes and data stores without even having all of them installed locally.

Codeship

Codeship

Codeship runs your automated tests and configured deployment when you push to your repository. It takes care of managing and scaling the infrastructure so that you are able to test and release more frequently and get faster feedback for building the product your users need.

CircleCI

CircleCI

Continuous integration and delivery platform helps software teams rapidly release code with confidence by automating the build, test, and deploy process. Offers a modern software development platform that lets teams ramp.

TeamCity

TeamCity

TeamCity is a user-friendly continuous integration (CI) server for professional developers, build engineers, and DevOps. It is trivial to setup and absolutely free for small teams and open source projects.

Drone.io

Drone.io

Drone is a hosted continuous integration service. It enables you to conveniently set up projects to automatically build, test, and deploy as you make changes to your code. Drone integrates seamlessly with Github, Bitbucket and Google Code as well as third party services such as Heroku, Dotcloud, Google AppEngine and more.

wercker

wercker

Wercker is a CI/CD developer automation platform designed for Microservices & Container Architecture.

GoCD

GoCD

GoCD is an open source continuous delivery server created by ThoughtWorks. GoCD offers business a first-class build and deployment engine for complete control and visibility.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Airflow

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

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