Google Cloud Deployment Manager vs Packer

Overview

Packer

Stacks573

Followers566

Votes41

Google Cloud Deployment Manager

Stacks24

Followers113

Votes5

Google Cloud Deployment Manager vs Packer: What are the differences?

Developers describe Google Cloud Deployment Manager as "Create and manage cloud resources with simple templates". Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml. On the other hand, Packer is detailed as "Create identical machine images for multiple platforms from a single source configuration". Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.

Google Cloud Deployment Manager and Packer can be categorized as "Infrastructure Build" tools.

Packer is an open source tool with 9.09K GitHub stars and 2.48K GitHub forks. Here's a link to Packer's open source repository on GitHub.

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Advice on Packer, Google Cloud Deployment Manager

Sung Won

Nov 4, 2019

Decidedon

Google Cloud IoT Core

Terraform

Python

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

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Comments

Timothy

SRE

Mar 20, 2020

Decided

I personally am not a huge fan of vendor lock in for multiple reasons:

I've seen cost saving moves to the cloud end up costing a fortune and trapping companies due to over utilization of cloud specific features.
I've seen S3 failures nearly take down half the internet.
I've seen companies get stuck in the cloud because they aren't built cloud agnostic.

I choose to use terraform for my cloud provisioning for these reasons:

It's cloud agnostic so I can use it no matter where I am.
It isn't difficult to use and uses a relatively easy to read language.
It tests infrastructure before running it, and enables me to see and keep changes up to date.
It runs from the same CLI I do most of my CM work from.

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Comments

Detailed Comparison

Packer	Google Cloud Deployment Manager
Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.	Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.
Super fast infrastructure deployment. Packer images allow you to launch completely provisioned and configured machines in seconds, rather than several minutes or hours.;Multi-provider portability. Because Packer creates identical images for multiple platforms, you can run production in AWS, staging/QA in a private cloud like OpenStack, and development in desktop virtualization solutions such as VMware or VirtualBox.;Improved stability. Packer installs and configures all the software for a machine at the time the image is built. If there are bugs in these scripts, they'll be caught early, rather than several minutes after a machine is launched.;Greater testability. After a machine image is built, that machine image can be quickly launched and smoke tested to verify that things appear to be working. If they are, you can be confident that any other machines launched from that image will function properly.	-
Statistics
Stacks 573	Stacks 24
Followers 566	Followers 113
Votes 41	Votes 5
Pros & Cons
Pros 27 Cross platform builds 8 Vm creation automation 4 Bake in security 1 Good documentation 1 Easy to use	Pros 2 Automates infrastructure deployments 1 Easy to deploy for GCP 1 Infrastracture as a code 1 Fast deploy and update Cons 1 Only using in GCP
Integrations
Amazon EC2 DigitalOcean Docker Google Compute Engine OpenStack VirtualBox	Jinja Python Google Cloud Storage Google Compute Engine Google Cloud SQL

What are some alternatives to Packer, Google Cloud Deployment Manager?

AWS CloudFormation

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.

Scalr

Scalr is a remote state & operations backend for Terraform with access controls, policy as code, and many quality of life features.

Pulumi

Pulumi is a cloud development platform that makes creating cloud programs easy and productive. Skip the YAML and just write code. Pulumi is multi-language, multi-cloud and fully extensible in both its engine and ecosystem of packages.

Azure Resource Manager

It is the deployment and management service for Azure. It provides a management layer that enables you to create, update, and delete resources in your Azure subscription. You use management features, like access control, locks, and tags, to secure and organize your resources after deployment.

Habitat

Habitat is a new approach to automation that focuses on the application instead of the infrastructure it runs on. With Habitat, the apps you build, deploy, and manage behave consistently in any runtime — metal, VMs, containers, and PaaS. You'll spend less time on the environment and more time building features.

AWS Cloud Development Kit

It is an open source software development framework to model and provision your cloud application resources using familiar programming languages. It uses the familiarity and expressive power of programming languages for modeling your applications. It provides you with high-level components that preconfigure cloud resources with proven defaults, so you can build cloud applications without needing to be an expert.

Yocto

It is an open source collaboration project that helps developers create custom Linux-based systems regardless of the hardware architecture. It provides a flexible set of tools and a space where embedded developers worldwide can share technologies, software stacks, configurations, and best practices that can be used to create tailored Linux images for embedded and IOT devices, or anywhere a customized Linux OS is needed.

GeoEngineer

GeoEngineer uses Terraform to plan and execute changes, so the DSL to describe resources is similar to Terraform's. GeoEngineer's DSL also provides programming and object oriented features like inheritance, abstraction, branching and looping.

Atlas

Atlas is one foundation to manage and provide visibility to your servers, containers, VMs, configuration management, service discovery, and additional operations services.