Ok, so first - AWS Copilot is CloudFormation under the hood, but the way it works results in you not thinking about CFN anymore. AWS found the right balance with Copilot - it's insanely simple to setup production-ready multi-account environment with many services inside, with CI/CD out of the box etc etc. It's pretty new, but even now it was enough to launch Transcripto, which uses may be a dozen of different AWS services, all bound together by Copilot.

Because Pulumi uses real programming languages, you can actually write abstractions for your infrastructure code, which is incredibly empowering. You still 'describe' your desired state, but by having a programming language at your fingers, you can factor out patterns, and package it up for easier consumption.

We use Terraform to manage AWS cloud environment for the project. It is pretty complex, largely static, security-focused, and constantly evolving.

Terraform provides descriptive (declarative) way of defining the target configuration, where it can work out the dependencies between configuration elements and apply differences without re-provisioning the entire cloud stack.

Terraform is vendor-neutral in a way that it is using a common configuration language (HCL) with plugins (providers) for multiple cloud and service providers.

Terraform keeps track of the previous state of the deployment and applies incremental changes, resulting in faster deployment times.

Terraform allows us to share reusable modules between projects. We have built an impressive library of modules internally, which makes it very easy to assemble a new project from pre-fabricated building blocks.

Software is imperfect, and Terraform is no exception. Occasionally we hit annoying bugs that we have to work around. The interaction with any underlying APIs is encapsulated inside 3rd party Terraform providers, and any bug fixes or new features require a provider release. Some providers have very poor coverage of the underlying APIs.

Terraform is not great for managing highly dynamic parts of cloud environments. That part is better delegated to other tools or scripts.

Terraform state may go out of sync with the target environment or with the source configuration, which often results in painful reconciliation.

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.

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

lxd/lxc and Docker aren't congruent so this comparison needs a more detailed look; but in short I can say: the lxd-integrated administration of storage including zfs with its snapshot capabilities as well as the system container (multi-process) approach of lxc vs. the limited single-process container approach of Docker is the main reason I chose lxd over Docker.

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless