AWS

Deploying apps and services to AWS using AWS Copilot CLI

Published Jan 3, 2024

Updated Jan 8, 2024

6 min read

Copilot has become a household name for developers, all thanks to GitHub’s popular AI tooling. Before GitHub released Copilot, AWS already had a developer tool in the wild, also named Copilot. I stumbled across AWS Copilot a year or two ago and found it to be a really great tool for easily deploying Serverless applications and services to AWS infrastructure. Since deploying to AWS has been such a huge pain point for so many developers, Copilot CLI is one of many tools that is designed to make this process a lot easier.

AWS Copilot is a command-line interface (CLI) that simplifies the process of deploying and managing containerized applications on AWS. It abstracts away the complexity of managing infrastructure, allowing us to focus on writing code. This blog post will provide an overview of AWS Copilot CLI and explore its practical use cases.

AWS Copilot CLI is designed to simplify the building, releasing, and operating of production-ready containerized applications on Amazon ECS and AWS Fargate. It offers an intuitive interface for developers to launch and manage environments, jobs, pipelines, and services in the cloud. Although ECS and Fargate are considered ‘Serverless’; you are actually deploying to more traditional, stateful, web servers on EC2 (these are cool again).

Installation

You can install the Copilot CLI using Homebrew.

brew install aws/tap/copilot-cli

Otherwise use one of the scripts from the installation page.

Credentials

You need to add AWS credentials with proper permissions to your ~/aws/.credentials file to use Copilot. See the credentials docs for more details on this.

[default]

aws_access_key_id=AKIAIOSFODNN7EXAMPLE
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

Overview

All you need is a Dockerfile that knows how to build and run your application and Copilot will handle the rest. Copilot provides a simple declarative set of commands, including examples and guided experiences built-in. These commands make it easy to start from scratch and get a containerized application running in the cloud in just a few steps. The configuration files that Copilot generates (called manifests) allow us to easily configure and adjust the amount of compute resources available to our web service (cpu, memory, instances, etc).

Here’s an architecture diagram for a load-balanced web service running on AWS and deployed with Copilot. The outermost layer is the region your application is deployed to on AWS ie: us-east-1, us-west-1, etc. Copilot handles all the networking for your application which includes a VPC, and public subnets that your server instances can be reached from. The load balancer (ALB) sits at the top, listens for requests, and directs traffic to the ECS Cluster (instances of your application server). Typically, the work involved with setting all of these pieces up and getting them working together is cumbersome to put it lightly.

Concepts

Before we dive into some of the commands, a quick look at the concepts that Copilot is built on so we have a clear understanding of what we can achieve with it.

Applications

Applications in Copilot is the top-level parent of all the AWS infrastructure managed by your Copilot setup. In this article I will generally refer to an application as any kind of software that you are deploying (api server, etc). In Copilot your ‘Application’ is the entire collection of all the environments and services that you have configured.

Here’s the diagram from the documentation. The Vote application can consist of a number of different services, jobs, pipelines, etc.

Services

In AWS Copilot, “Services” refer to the type of application (not to be confused with the definition of Application in the previous section) that you’re deploying and the underlying AWS service infrastructure that supports it.

Using the Load Balanced Web Service service from the diagram above, the service consists of the ECS (Elastic Container Service) service, the application load balancer, and the network load balancer. These are some of the AWS infrastructure that Copilot orchestrates for you when deploying this type of “Service”.

There are a few main types of services that you can deploy with Copilot

Internet-facing web services (Static Site, Load Balanced Web Service, etc)
Backend services (services that can only be accessed internally)
Worker services (pub/sub queues, etc)

Environments

When setting up your project and your first service you will supply Copilot with the name of the environment you want to deploy to. For example, you can create a production environment initially to deploy your application and services to and then later on add additional environments like a staging environment.

Jobs

You might also know these as ‘crons’ but these are just tasks or some code that runs on a schedule.

Pipelines

Pipelines are for automating tests and releases. This is AWS’s CI/CD service somewhat similar to something like GitHub actions. Copilot can initialize and create new pipelines for you. We can configure this as a manifest in our codebase that declares instructions on how we build and deploy our application(s).

Configuration

Copilot is configured through various configuration files called ‘Manifests’. The documentation contains examples on how to configure your application, services, environments, jobs, and pipelines. You can also refer to it to learn what options are available. Since Copilot is a CLI tool they make a lot of the configuration process pretty painless by providing walkthrough prompts and generating configuration files for you.

The CLI

Now that we have a pretty good idea of what AWS Copilot is and the types of things we can accomplish with it, let's walk through using the CLI.

copilot init

This is where you will most likely want to start to get your application ready for deployment to AWS. The init command will prompt you with questions like what kind of service you want to generate and once you’re done, it will generate all the initial configuration files for you.

Example setup:

> copilot init
Welcome to the Copilot CLI! We're going to walk you through some questions
to help you get set up with a containerized application on AWS. An application is a collection of
containerized services that operate together.

> Use existing application: No
> Application name: twitter

Which workload type best represents your architecture?  [Use arrows to move, type to filter, ? for more help]

Request-Driven Web Service  (App Runner)
Load Balanced Web Service   (Internet to ECS on Fargate)
Backend Service             (ECS on Fargate)
Worker Service              (Events to SQS to ECS on Fargate)
Static Site                 (Internet to CDN to S3 bucket)
Scheduled Job               (Scheduled event to State Machine to Fargate)

You’ll pick your service type, tell Copilot where your Dockerfile lives, name your environment, and let it do its magic from there. Copilot will generate your Manifest/config files which in turn become CloudFormation stacks that set up all your infrastructure on AWS.

Other Commands

There are commands for each concept that we covered above. There are Create, Delete, and Deploy operation commands for Environments, Services, Jobs, and Pipelines.

If you wanted to add a staging environment to your application you could just run copilot env init .

There are also commands to manage your Application or things like tasks. To see details about your Application you can run copilot app show -n [name]

Conclusion

AWS Copilot CLI is a powerful tool that simplifies the deployment and management of containerized applications on AWS. It abstracts away the complexities of the underlying infrastructure, allowing developers to focus on writing code and delivering value to their customers. Whether you're deploying a simple web service or managing multiple environments, AWS Copilot CLI can make your cloud development process more efficient and enjoyable.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Dane Grant
JavaScript developer building things on web, native, and server environments. Free time filled with reading, cooking, dog, sports, comedy, and music.
@danecando @danecando

How to set up local cloud environment with LocalStack

How to set up local cloud environment with LocalStack Developers enjoy building applications with AWS due to the richness of their solutions to problems. However, testing an AWS application during development without a dedicated AWS account can be challenging. This can slow the development process and potentially lead to unnecessary costs if the AWS account isn't properly managed. This article will examine LocalStack, a development framework for developing and testing AWS applications, how it works, and how to set it up. Assumptions This article assumes you have a basic understanding of: - AWS: Familiarity with S3, CloudFormation, and SQS. - Command Line Interface (CLI): Comfortable running commands in a terminal or command prompt. - JavaScript and Node.js: Basic knowledge of JavaScript and Node.js, as we will write some code to interact with AWS services. - Docker Concepts: Understanding of Docker basics, such as images and containers, since LocalStack runs within a Docker container. What is LocalStack? LocalStack is a cloud service emulator that runs in a single container on your laptop or in your CI environment. With LocalStack, you can run your AWS applications or Lambdas entirely on your local machine without connecting to a remote cloud provider! Whether you are testing complex CDK applications or Terraform configurations or just beginning to learn about AWS, LocalStack simplifies your testing and development workflow, relieving you from the complexity of testing AWS applications. Prerequisite Before setting up LocalStack, ensure you have the following: 1. Docker Installed: LocalStack runs in a Docker container, so you need Docker installed on your machine. You can download and install Docker from here. 2. Node.js and npm: Ensure you have Node.js and npm installed, as we will use a simple Node.js application to test AWS services. You can download Node.js from here. 3. Python: Python is required for installing certain CLI tools that interact with LocalStack. Ensure you have Python 3 installed on your machine. You can download Python from here. Installation In this article, we will use the LocalStack CLI, which is the quickest way to get started with LocalStack. It allows you to start LocalStack from your command line. Localstack spins up a Docker instance, Alternative methods of managing the LocalStack container exist, and you can find them here. To install LocalStack CLI, you can use homebrew by running the following command: ` If you do not use a macOS or you don’t have Brew installed, you can install the CLI using Python: ` To confirm your installation was successful, run the following: ` That should output the installed version of LocalStack. Now you can start LocalStack by running the following command: ` This command will start LocalStack in docker mode, and since it is your first installation, try to pull the LocalStack image. You should see the below on your terminal After the image is downloaded successfully, the docker instance is spun up, and LocalStack is running on your machine on port 4566. Testing AWS Services with LocalStack LocalStack lets you easily test AWS services during development. It supports many AWS products but has some limitations, and not all features are free. Community Version: Free access to core AWS products like S3, SQS, DynamoDB, and Lambda. Pro Version: Access to more AWS products and enhanced features. Check the supported community and pro version resources for more details. We're using the community edition, and the screenshot below shows its supported products. To see the current products supported in the community edition, visit http://localhost:4566/_localstack/health. This article will test AWS CloudFormation and SQS. Before we can start testing, we need to create a simple Node.js app. On your terminal, navigate to the desired folder and run the following command: ` This command will create a package.json file at the root of the directory. Now we need to install aws-sdk. Run the following command: ` With that installed, we can now start testing various services. AWS CloudFormation AWS CloudFormation is a service that allows users to create, update, and delete resources in an AWS account. This service can also automate the process of provisioning and configuring resources. We are going to be using LocalStack to test creating a CloudFormation template. In the root of the folder, create a file called cloud-formation.js. This file will be used to create a CloudFormation stack that will be used to create an S3 bucket. Add the following code to the file: ` In the above code, we import the aws-sdk package, which provides the necessary tools to interact with AWS services. Then, an instance of the AWS.CloudFormation class is created. This instance is configured with: region: The AWS region where the requests are sent. In this case, us-east-1 is the default region for LocalStack. endpoint: The URI to send requests to is set to http://localhost:4566 for LocalStack. You should configure this with environment variables to switch between LocalStack for development and the actual AWS endpoint for production, ensuring the same code can be used in both environments. credentials: The AWS credentials to sign requests with. We are passing new AWS.Credentials("test", "test") The params object defines the parameters needed to create the CloudFormation stack: StackName: The name of the stack. Here, we are using 'test-local-stack'. TemplateBody: A JSON string representing the CloudFormation template. In this example, it defines a single resource, an S3 bucket named TestBucket. The createStack method is called on the CloudFormation client with the params object. This method attempts to create the stack. If there is an error, we log it to the console else, we log the successful data to the console. Now, let’s test the code by running the following command: ` If we run the above command, we should see the JSON response on the terminal. ` AWS SQS The process for testing SQS with LocalStack using the aws-sdk follows the same pattern as above, except that we will introduce another CLI package, awslocal. awslocal is a thin wrapper and a substitute for the standard aws command, enabling you to run AWS CLI commands within the LocalStack environment without specifying the --endpoint-url parameter or a profile. To install awslocal, run the following command on your terminal: ` Next, let’s create an SQS queue using the following command: ` This will create a queue name test-queue and return queueUrl like below: ` Now, in our directory, let’s create a sqs.js file, and inside of it, let’s paste the following code: ` In the above code, an instance of the AWS.SQS class is created. The instance is configured with the same parameters as when creating the CloudFormation. We also created a params object which had the required properties needed to send a SQS message: QueueUrl: The URL of the Amazon SQS queue to which a message is sent. In our case, it will be the URL we got when we created a local SQS. Make sure to manage this in environment variables to switch between LocalStack for development and the actual AWS queue URL for production, ensuring the same code can be used in both environments. MessageBody: The message to send. We call the sendMessage method, passing the params object and a callback that handles error and data, respectively. Let’s run the code using the following command: ` We should get a JSON object in the terminal like the following: ` To test if we can receive the SQS message sent, let’s create a sqs-receive.js. Inside the file, we can copy over the AWS.SQS instance that was created earlier into the file and add the following code: ` Run the code using the following command: ` We should receive a JSON object and should be able to see the previous message we sent ` When you are done with testing, you can shut down LocalStack by running the following command: ` Conclusion In this article, we looked at how to set up a local cloud environment using LocalStack, a powerful tool for developing and testing AWS applications locally. We walked through the installation process of LocalStack and demonstrated how to test AWS services using the AWS SDK, including CloudFormation and SQS. Setting up LocalStack allows you to simulate various AWS services on your local machine, which helps streamline development workflows and improve productivity. Whether you are testing simple configurations or complex deployments, LocalStack provides the environment to ensure your applications work as expected before moving to a production environment. Using LocalStack, you can confidently develop and test your AWS applications without an active AWS account, making it an invaluable tool for developers looking to optimize their development process....

Jun 19, 2024

6 mins

AWSDevOps

Building Scalable AI Applications: Insights from AWS's Michael Liendo

In this episode of the Modern Web Podcast, Rob Ocel, Danny Thompson, and Adam Rackis talk with Michael Liendo, Senior Developer Advocate at AWS, about building practical AI applications and tackling challenges like scalability, multimodal functionality, and cloud infrastructure choices. Michael shares insights on tools like AWS Amplify and DynamoDB, discusses strategies for managing cloud costs, and explores the evolving role of prompt engineering. Michael previews his upcoming talks at AWS re:Invent on AI and scalable B2B SaaS applications. Chapters - 00:00 - Introduction and Guest Welcome - 01:30 - Talking Weather and Life in the Midwest - 03:00 - Exploring Generative AI and Practical Applications - 06:45 - Navigating Cloud Costs and Scalability Considerations - 08:30 - Maintaining Creativity and Customization with AI - 11:00 - Managed Services vs. On-Prem Infrastructure Debate - 15:30 - Choosing a Tech Stack for Side Projects and Startups - 18:45 - Learning Cloud: Paths for Full-Stack Cloud Development - 22:30 - The Role of Cloud Certifications in Today's Market - 26:00 - Preview of Michael’s Upcoming Talks at AWS re:Invent - 32:00 - Where to Find Michael Online Follow Michael Liendo on Social Media Twitter: https://x.com/focusotter Linkedin: https://www.linkedin.com/in/focusotter/ Sponsored by Wix Studio...

Oct 14, 2024

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AIAws AmplifyAWS

Remix's evolution to a Vite plugin

The Remix / React Router news is a beautiful illustration of software evolution. You start with a clear idea of what you’re building, but it’s impossible to anticipate exactly what it will become. If you haven’t heard the news yet, Ryan Florence announced at React Conf 2024 that Remix will be a Vite plugin for React Router. It’s a sort of surprising announcement but it makes a lot of sense if you’ve been following along as Remix the project has evolved. In this post we’ll recap the evolution of the project and then dig into what this change means for the future of Remix in the Server Components era of React. 🗓️ October 2021 > Remix is open sourced In the beginning, Remix cost money. In October 2021 they received some seed funding and the project was open sourced. Remix was an exciting new framework for server-rendering React websites that included API’s for loading data on the server and submitting data to the server with forms. It started a trend in the front-end framework space that a lot of other popular frameworks have modeled after or taken inspiration from. 🗓️ March 2022 - September 2022 > Remixing React Router At some point they realized that the API’s that they had created for Remix were a more natural fit in React Router. In this post Ryan details the plans to move these Remix API’s into React Router. Several months later, React Router 6.4 is released with the action, loader, and other Remix API’s baked in. > "We've always thought of Remix as "just a compiler and server for React Router" After this release, Remix is now mostly as they described - a compiler and server for React Router. 🗓️ October 2023 - February 2024 > Remix gets Vite support If you’re not familiar with Vite, it’s an extremely popular tool for front-end development that handles things like development servers and code bundling/compiling. A lot of the modern front-end frameworks are built on top of it. Remix announced support for Vite as an alternative option to their existing compiler and server. Since Remix is now mostly “just a compiler and server for React Router” - what’s left of Remix now that Vite can handle those things? 🗓️ May 2024 > Remix is React Router The Remix team releases a post detailing the announcement that Ryan made at React Conf. Looking back through the evolution of the project this doesn’t seem like an out of the blue, crazy announcement. Merging Remix API’s into React Router and then replacing the compiler and server with Vite didn’t leave much for Remix to do as a standalone project. 🐐 React Router - the past, present, and future of React React Router is one of the oldest, and most used packages in the React ecosystem. > Since Remix has always been effectively "React Router: The Framework", we wanted to create a bridge for all these React Router projects to be able to upgrade to Remix. With Remix being baked into React Router and now supporting SPA mode, legacy React Router applications have an easier path for migrating to the next generation of React. https://x.com/ryanflorence/status/1791488550162370709 Remix vs React 19 If you’ve taken a look at React 19 at all, it turns out it now handles a lot of the problems that Remix set out to solve in the first place. Things like Server Components, Server Actions, and Form Actions provide the same sort of programming model that Remix popularized. https://x.com/ryanflorence/status/1791484663883829258 Planned Changes for Remix and React Router Just recently, a post was published giving the exact details of what the changes to Remix and React Router will look like. tl;dr For React Router * React Router v6 to v7 will be a non-breaking upgrade * The Vite plugin from Remix is coming to React Router in v7 * The Vite plugin simply makes existing React Router features more convenient to use, but it isn't required to use React Router v7 * v7 will support both React 18 and React 19 For Remix * What would have been Remix v3 is React Router v7 * Remix v2 to React Router v7 will be a non-breaking upgrade * Remix is coming back better than ever in a future release with an incremental adoption strategy enabled by these changes For Both * React Router v7 comes with new features not in Remix or React Router today: RSC, server actions, static pre-rendering, and enhanced Type Safety across the board The show goes on I love how this project has evolved and I tip my hat to the Remix/React Router team. React Router feels like a cozy blanket in an ecosystem that is going through some major changes which feels uncertain and a little scary at times. The Remix / React Router story is still being written and I’m excited to see where it goes. I’m looking forward to seeing the work they are doing to support React Server Components. A lot of people are pretty excited about RSC’s and there is a lot of room for unique and interesting implementations. Long live React Router 🙌....

Jul 10, 2024

4 mins

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Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

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Deploying apps and services to AWS using AWS Copilot CLI

Installation

Credentials

Overview

Concepts

Applications

Services

Environments

Jobs

Pipelines

Configuration

The CLI

Other Commands

Conclusion

Dane Grant

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Let's innovate together!

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