What is AWS CLI and what are its top alternatives?
Top Alternatives to AWS CLI
PowerShell
A command-line shell and scripting language built on .NET. Helps system administrators and power-users rapidly automate tasks that manage operating systems (Linux, macOS, and Windows) and processes. ...
AWS Shell
The AWS Command Line Interface is a unified tool to manage your AWS services. ...
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. ...
Putty
It is an SSH and telnet client, developed originally by Simon Tatham for the Windows platform. It is open source software that is available with source code and is developed and supported by a group of volunteers. ...
AWS Amplify
A JavaScript library for frontend and mobile developers building cloud-enabled applications. The library is a declarative interface across different categories of operations in order to make common tasks easier to add into your application. The default implementation works with Amazon Web Services (AWS) resources but is designed to be open and pluggable for usage with other cloud services that wish to provide an implementation or custom backends. ...
LocalStack
LocalStack provides an easy-to-use test/mocking framework for developing Cloud applications. ...
troposphere
The troposphere library allows for easier creation of the AWS CloudFormation JSON by writing Python code to describe the AWS resources. troposphere also includes some basic support for OpenStack resources via Heat. ...
awless
awless is a fast, powerful and easy-to-use command line interface (CLI) to manage Amazon Web Services. ...
AWS CLI alternatives & related posts
Terraform
related Terraform posts
Google Cloud IoT Core
Terraform
Python
Google Cloud Deployment Manager
Google Cloud Build
Google Cloud Run
Google Cloud Bigtable
Google BigQuery
Google Cloud Storage
Google Compute Engine
GitHubContext: 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
MongoDB Atlas
Java
Spring Boot
Node.js
ExpressJSPython
Flask
Amazon Kinesis
Amazon Kinesis Firehose
Amazon SNS
Amazon SQS
AWS Lambda
Angular 2
RxJSGitHub
Travis CITerraform
Docker
Serverless
Amazon RDS
Amazon DynamoDB
Amazon S3We 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