Alternatives to AWS CodeDeploy logo

Alternatives to AWS CodeDeploy

AWS CodePipeline, Jenkins, Docker, Ansible, and Chef are the most popular alternatives and competitors to AWS CodeDeploy.
337
490
+ 1
38

What is AWS CodeDeploy and what are its top alternatives?

AWS CodeDeploy is a service that automates code deployments to Amazon EC2 instances. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during deployment, and handles the complexity of updating your applications.
AWS CodeDeploy is a tool in the Deployment as a Service category of a tech stack.

Top Alternatives to AWS CodeDeploy

  • AWS CodePipeline

    AWS CodePipeline

    CodePipeline builds, tests, and deploys your code every time there is a code change, based on the release process models you define. ...

  • 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. ...

  • Docker

    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...

  • Ansible

    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Chef

    Chef

    Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others. ...

  • AWS CodeBuild

    AWS CodeBuild

    AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. ...

  • AWS CodeCommit

    AWS CodeCommit

    CodeCommit eliminates the need to operate your own source control system or worry about scaling its infrastructure. You can use CodeCommit to securely store anything from source code to binaries, and it works seamlessly with your existing Git tools. ...

  • Terraform

    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

AWS CodeDeploy alternatives & related posts

AWS CodePipeline logo

AWS CodePipeline

431
692
30
Continuous delivery service for fast and reliable application updates
431
692
+ 1
30
PROS OF AWS CODEPIPELINE
  • 13
    Simple to set up
  • 8
    Managed service
  • 4
    GitHub integration
  • 3
    Parallel Execution
  • 2
    Automatic deployment
  • 0
    Manual Steps Available
CONS OF AWS CODEPIPELINE
  • 2
    No project boards
  • 1
    No integration with "Power" 365 tools

related AWS CodePipeline posts

Khauth György
CTO at SalesAutopilot Kft. · | 12 upvotes · 378.2K views

I'm the CTO of a marketing automation SaaS. Because of the continuously increasing load we moved to the AWSCloud. We are using more and more features of AWS: Amazon CloudWatch, Amazon SNS, Amazon CloudFront, Amazon Route 53 and so on.

Our main Database is MySQL but for the hundreds of GB document data we use MongoDB more and more. We started to use Redis for cache and other time sensitive operations.

On the front-end we use jQuery UI + Smarty but now we refactor our app to use Vue.js with Vuetify. Because our app is relatively complex we need to use vuex as well.

On the development side we use GitHub as our main repo, Docker for local and server environment and Jenkins and AWS CodePipeline for Continuous Integration.

See more
Oliver Burn

We recently added new APIs to Jira to associate information about Builds and Deployments to Jira issues.

The new APIs were developed using a spec-first API approach for speed and sanity. The details of this approach are described in this blog post, and we relied on using Swagger and associated tools like Swagger UI.

A new service was created for managing the data. It provides a REST API for external use, and an internal API based on GraphQL. The service is built using Kotlin for increased developer productivity and happiness, and the Spring-Boot framework. PostgreSQL was chosen for the persistence layer, as we have non-trivial requirements that cannot be easily implemented on top of a key-value store.

The front-end has been built using React and querying the back-end service using an internal GraphQL API. We have plans of providing a public GraphQL API in the future.

New Jira Integrations: Bitbucket CircleCI AWS CodePipeline Octopus Deploy jFrog Azure Pipelines

See more
Jenkins logo

Jenkins

42.4K
34.9K
2.2K
An extendable open source continuous integration server
42.4K
34.9K
+ 1
2.2K
PROS OF JENKINS
  • 522
    Hosted internally
  • 465
    Free open source
  • 315
    Great to build, deploy or launch anything async
  • 243
    Tons of integrations
  • 210
    Rich set of plugins with good documentation
  • 109
    Has support for build pipelines
  • 72
    Open source and tons of integrations
  • 63
    Easy setup
  • 61
    It is open-source
  • 54
    Workflow plugin
  • 11
    Configuration as code
  • 10
    Very powerful tool
  • 9
    Many Plugins
  • 8
    Continuous Integration
  • 8
    Great flexibility
  • 8
    Git and Maven integration is better
  • 6
    Github integration
  • 6
    100% free and open source
  • 6
    Slack Integration (plugin)
  • 5
    Easy customisation
  • 5
    Self-hosted GitLab Integration (plugin)
  • 4
    Docker support
  • 3
    Excellent docker integration
  • 3
    Platform idnependency
  • 3
    Fast builds
  • 3
    Pipeline API
  • 2
    Customizable
  • 2
    Can be run as a Docker container
  • 2
    It`w worked
  • 2
    JOBDSL
  • 2
    Hosted Externally
  • 2
    It's Everywhere
  • 2
    AWS Integration
  • 1
    NodeJS Support
  • 1
    PHP Support
  • 1
    Ruby/Rails Support
  • 1
    Universal controller
  • 1
    Easily extendable with seamless integration
  • 1
    Build PR Branch Only
CONS OF JENKINS
  • 12
    Workarounds needed for basic requirements
  • 8
    Groovy with cumbersome syntax
  • 6
    Limited abilities with declarative pipelines
  • 6
    Plugins compatibility issues
  • 5
    Lack of support
  • 4
    No YAML syntax
  • 2
    Too tied to plugins versions

related Jenkins posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Thierry Schellenbach

Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

#ContinuousIntegration #CodeCollaborationVersionControl

See more
Docker logo

Docker

112.1K
89.2K
3.8K
Enterprise Container Platform for High-Velocity Innovation.
112.1K
89.2K
+ 1
3.8K
PROS OF DOCKER
  • 821
    Rapid integration and build up
  • 688
    Isolation
  • 517
    Open source
  • 505
    Testa­bil­i­ty and re­pro­ducibil­i­ty
  • 459
    Lightweight
  • 217
    Standardization
  • 182
    Scalable
  • 105
    Upgrading / down­grad­ing / ap­pli­ca­tion versions
  • 86
    Security
  • 84
    Private paas environments
  • 33
    Portability
  • 25
    Limit resource usage
  • 15
    I love the way docker has changed virtualization
  • 15
    Game changer
  • 12
    Fast
  • 11
    Concurrency
  • 7
    Docker's Compose tools
  • 4
    Fast and Portable
  • 4
    Easy setup
  • 4
    Because its fun
  • 3
    Makes shipping to production very simple
  • 2
    It's dope
  • 1
    Highly useful
  • 1
    MacOS support FAKE
  • 1
    Its cool
  • 1
    Docker hub for the FTW
  • 1
    Very easy to setup integrate and build
  • 1
    Package the environment with the application
  • 1
    Does a nice job hogging memory
  • 1
    Open source and highly configurable
  • 1
    Simplicity, isolation, resource effective
CONS OF DOCKER
  • 7
    New versions == broken features
  • 5
    Documentation not always in sync
  • 5
    Unreliable networking
  • 3
    Moves quickly
  • 2
    Not Secure

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.1M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
See more
Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Ansible logo

Ansible

13.8K
11K
1.3K
Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
13.8K
11K
+ 1
1.3K
PROS OF ANSIBLE
  • 275
    Agentless
  • 204
    Great configuration
  • 194
    Simple
  • 173
    Powerful
  • 151
    Easy to learn
  • 66
    Flexible
  • 54
    Doesn't get in the way of getting s--- done
  • 33
    Makes sense
  • 29
    Super efficient and flexible
  • 27
    Powerful
  • 11
    Dynamic Inventory
  • 8
    Backed by Red Hat
  • 7
    Works with AWS
  • 6
    Easy to maintain
  • 6
    Cloud Oriented
  • 4
    Simple
  • 4
    Because SSH
  • 4
    Multi language
  • 4
    Easy
  • 4
    Procedural or declarative, or both
  • 4
    Simple and powerful
  • 3
    Consistency
  • 3
    Vagrant provisioner
  • 2
    Debugging is simple
  • 2
    Well-documented
  • 2
    Fast as hell
  • 2
    Masterless
  • 2
    Merge hash to get final configuration similar to hiera
  • 1
    Certified Content
  • 1
    Work on windows, but difficult to manage
CONS OF ANSIBLE
  • 5
    Dangerous
  • 5
    Hard to install
  • 3
    Bloated
  • 3
    Backward compatibility
  • 2
    Doesn't Run on Windows
  • 2
    No immutable infrastructure

related Ansible posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Sebastian Gębski

Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

See more
Chef logo

Chef

1.1K
964
344
Build, destroy and rebuild servers on any public or private cloud
1.1K
964
+ 1
344
PROS OF CHEF
  • 109
    Dynamic and idempotent server configuration
  • 76
    Reusable components
  • 47
    Integration testing with Vagrant
  • 43
    Repeatable
  • 30
    Mock testing with Chefspec
  • 14
    Ruby
  • 8
    Can package cookbooks to guarantee repeatability
  • 7
    Works with AWS
  • 3
    Has marketplace where you get readymade cookbooks
  • 3
    Matured product with good community support
  • 2
    Less declarative more procedural
  • 2
    Open source configuration mgmt made easy(ish)
CONS OF CHEF
    Be the first to leave a con

    related Chef posts

    In late 2013, the Operations Engineering team at PagerDuty was made up of 4 engineers, and was comprised of generalists, each of whom had one or two areas of depth. Although the Operations Team ran its own on-call, each engineering team at PagerDuty also participated on the pager.

    The Operations Engineering Team owned 150+ servers spanning multiple cloud providers, and used Chef to automate their infrastructure across the various cloud providers with a mix of completely custom cookbooks and customized community cookbooks.

    Custom cookbooks were managed by Berkshelf, andach custom cookbook contained its own tests based on ChefSpec 3, coupled with Rspec.

    Jenkins was used to GitHub for new changes and to handle unit testing of those features.

    See more
    Marcel Kornegoor

    Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

    For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

    For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

    Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

    See more
    AWS CodeBuild logo

    AWS CodeBuild

    324
    369
    38
    Build and test code with continuous scaling
    324
    369
    + 1
    38
    PROS OF AWS CODEBUILD
    • 6
      Pay per minute
    • 4
      Parameter Store integration for passing secrets
    • 4
      Integrated with AWS
    • 3
      Bit bucket integration
    • 2
      GitHub Webhooks support
    • 2
      Streaming logs to Amazon CloudWatch
    • 1
      Local build debug support
    • 1
      Native support for accessing Amazon VPC resources
    • 1
      VPC PrivateLinks to invoke service without internet
    • 1
      Docker based build environment
    • 1
      Support for bringing custom Docker images
    • 1
      Fully managed (no installation/updates, servers to mai
    • 1
      PCI, SOC, ISO, HIPAA compliant
    • 1
      Full API/SDKs/CLI support
    • 1
      YAML based configuration
    • 1
      Great support (forums, premium support, SO, GitHub)
    • 1
      Perpetual free tier option (100 mins/month)
    • 1
      AWS Config and Config rule integration for compliance
    • 1
      GitHub Enterprise support
    • 1
      Windows/.NET support
    • 1
      Jenkins plugin integration
    • 1
      Ondemand scaling of build jobs
    • 1
      Scheduled builds with CloudWatch Events integration
    • 0
      G
    CONS OF AWS CODEBUILD
      Be the first to leave a con

      related AWS CodeBuild posts

      Chris McFadden
      VP, Engineering at SparkPost · | 9 upvotes · 131.3K views

      The recent move of our CI/CD tooling to AWS CodeBuild / AWS CodeDeploy (with GitHub ) as well as moving to Amazon EC2 Container Service / AWS Lambda for our deployment architecture for most of our services has helped us significantly reduce our deployment times while improving both feature velocity and overall reliability. In one extreme case, we got one service down from 90 minutes to a very reasonable 15 minutes. Container-based build and deployments have made so many things simpler and easier and the integration between the tools has been helpful. There is still some work to do on our service mesh & API proxy approach to further simplify our environment.

      See more

      Hi, I need advice. In my project, we are using Bitbucket hosted on-prem, Jenkins, and Jira. Also, we have restrictions not to use any plugins for code review, code quality, code security, etc., with bitbucket. Now we want to migrate to AWS CodeCommit, which would mean that we can use, let's say, Amazon CodeGuru for code reviews and move to AWS CodeBuild and AWS CodePipeline for build automation in the future rather than using Jenkins.

      Now I want advice on below.

      1. Is it a good idea to migrate from Bitbucket to AWS Codecommit?
      2. If we want to integrate Jira with AWS Codecommit, then how can we do this? If a developer makes any changes in Jira, then a build should be triggered automatically in AWS and create a Jira ticket if the build fails. So, how can we achieve this?
      See more
      AWS CodeCommit logo

      AWS CodeCommit

      287
      645
      188
      Fully-managed source control service that makes it easy for companies to host secure and highly scalable private Git...
      287
      645
      + 1
      188
      PROS OF AWS CODECOMMIT
      • 43
        Free private repos
      • 26
        IAM integration
      • 23
        Pay-As-You-Go Pricing
      • 19
        Amazon feels the most Secure
      • 18
        Repo data encrypted at rest
      • 11
        I can make repository by myself if I have AWS account
      • 11
        Faster deployments when using other AWS services
      • 7
        AWS CodePipeline integration
      • 6
        Codebuild integration
      • 6
        Does not support web hooks yet! :(
      • 4
        Cost Effective
      • 2
        No Git LFS! Dealbreaker for me
      • 2
        Elastic Beanstalk Integration
      • 2
        Integrated with AWS Ecosystem
      • 1
        Only US Region
      • 1
        Issue tracker
      • 1
        Open source friendly
      • 1
        Available in Ireland (Dublin) region
      • 1
        CodeDeploy Integration
      • 1
        CodeCommit Trigger for an AWS Lambda Function
      • 1
        Integration via SQS/SNS for events (replaces webhooks)
      • 1
        IAM
      • 0
        Ui
      CONS OF AWS CODECOMMIT
      • 11
        UI sucks
      • 4
        SLOW
      • 3
        No Issue Tracker
      • 2
        Bad diffing/no blame
      • 2
        No fork
      • 2
        No webhooks
      • 2
        NO LFS support
      • 1
        Can't download file from UI
      • 1
        Only time based triggers
      • 0
        Accident-prone UI

      related AWS CodeCommit posts

      Hi, I need advice. In my project, we are using Bitbucket hosted on-prem, Jenkins, and Jira. Also, we have restrictions not to use any plugins for code review, code quality, code security, etc., with bitbucket. Now we want to migrate to AWS CodeCommit, which would mean that we can use, let's say, Amazon CodeGuru for code reviews and move to AWS CodeBuild and AWS CodePipeline for build automation in the future rather than using Jenkins.

      Now I want advice on below.

      1. Is it a good idea to migrate from Bitbucket to AWS Codecommit?
      2. If we want to integrate Jira with AWS Codecommit, then how can we do this? If a developer makes any changes in Jira, then a build should be triggered automatically in AWS and create a Jira ticket if the build fails. So, how can we achieve this?
      See more

      Mojolicious Perl Redmine Redis AWS CodeCommit Amazon SES PostgreSQL Postman Docker jQuery VirtualBox Sublime Text GitHub Git GitLab CI @DBIx::Class @metacpan @TheBat

      See more
      Terraform logo

      Terraform

      10.9K
      8.1K
      318
      Describe your complete infrastructure as code and build resources across providers
      10.9K
      8.1K
      + 1
      318
      PROS OF TERRAFORM
      • 108
        Infrastructure as code
      • 72
        Declarative syntax
      • 44
        Planning
      • 27
        Simple
      • 24
        Parallelism
      • 7
        Cloud agnostic
      • 6
        It's like coding your infrastructure in simple English
      • 5
        Well-documented
      • 4
        Automates infrastructure deployments
      • 4
        Immutable infrastructure
      • 4
        Platform agnostic
      • 3
        Portability
      • 3
        Extendable
      • 3
        Automation
      • 2
        Scales to hundreds of hosts
      • 2
        Lightweight
      CONS OF TERRAFORM
      • 1
        Doesn't have full support to GKE

      related Terraform posts

      Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

      Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

      Check Out My Architecture: CLICK ME

      Check out the GitHub repo attached

      See more
      Emanuel Evans
      Senior Architect at Rainforest QA · | 15 upvotes · 674.1K views

      We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

      We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

      Read the blog post to go more in depth.

      See more