In a previous article we’ve seen how to deploy a Function App using the Azure CLI and the Azure Functions Core Tools. In this article we’ll see how to get rid of the help of the latter and use the Zip Deploy feauture. Fixing the script. We already have a deployment script. It is something like this: #!/bin/bash location=$2 stage=$3 serviceName=$1-$stage resourceGroupName=$serviceName"-rg" serviceNameToLower="$(echo $serviceName | tr '[:upper:]' '[:lower:]')" storageAccountName="$(echo ${serviceNameToLower//"-"/""})" echo "Creating resource group" az group create --name $resourceGroupName --location $location echo "Creating storage account" az storage account create --name $storageAccountName --location $location --resource-group $resourceGroupName --sku Standard_LRS echo "Creating function app" az functionapp create --name $serviceName --storage-account $storageAccountName --consumption-plan-location westeurope --resource-group $resourceGroupName echo "Publishing function locally" dotnet build echo "Publisihng function to Azure" func azure functionapp publish $serviceName What are we going to do is to change the last two steps.

In previous articles (I, II) we’ve seen how to deploy an Azure Function App using the Azure CLI and the Azure Functions Core Tools. In this article we’ll see how to deploy it using Terraform. Prerequisites In order to follow this article you will need the .Net SDK 2.1, the Azure CLI and Terraform installed in your laptop/container/VM/whatever. Building the package What Terraform is going to do is take advantage of the Zip deployment capability (More on this in a future article).

In the last article we saw how to deploy an azure function from the CLI. In this article we’ll see how we can use the same script to deploy the function from a continuous integration environment. In this case we’ll use CircleCI. Creating a new docker image As we saw, if we want to deploy the Function App using the same script we’d need to use the Azure CLI and the Azure Functions Core Tools.

In the last few days, I’ve been tinkering with Azure Functions, reading the documentation a bit and doing a Pluralsight course. As it happens quite offten, these introductory courses use easy techniques to deploy the code, focusing on showing what you can do with the platform. Although obviously this has some value, I don’t think it’s a good idea because, at the end, it will be something that you won’t be able to use in a serious test.

In the previous article we saw how to create a basic deployment pipeline for a serverless application. In this article, we’re going to enrich the deployment by allowing to have different values for configuration settings in each stage. Background The moment your application starts to be a little bit more complex, you need to use configuration settings. These settings can be things like the log level, addresses of external services, usernames and (encrypted) passwords, etc.

Starting with AWS Lambda is really easy. You can even write a function in the browser! But that’s not how you should work on a daily basis. You must have a CI/CD pipeline set up, you probably have two accounts (one for production and another one for development), you need a repeatable and reliable way to create your infrastructure and so on. In this article I’ll show you how to create a simple continuous delivery pipeline that brings us closer to a professional application development.

In the previous article we saw how we can configure Vault and write and read static secrets. Today, we’re going to see how we can use Vault to generate temporary users on a MySQL server so we can control access in a more secure way. First of all we’ll need a MySQL server connected to the same network than the Vault server. Let’s change the docker-compose.yml file to accomplish this.

Vault from HashiCorp is an amazing tool to manage the secrets on your organisation. It not only can help you to manage what they call static secrets that you can write and read, but also allows you to manage dynamic secrets to, for example, create temporary users in a MySQL database with certain permissions. It helps you to have a more secure organization. Today we’re going to see how can we configure and use the basics of Vault.

When you start working on a new project, there are a couple of things that you should try to discover as fast as you can: the shape of the code and the internals of the team you’re working with. You can (and should) try to discover both of them using conversations (at the end, developing software is having conversations (link in spanish)). But it’s also useful to try to discover this things for yourself, to confirm what the conversations are saying, or to bo a little bit faster.

In the last few weeks I’ve been working on Crystal Gazer, a nodejs console application to gather information from your Git repository. You can find it on GitHub and on NPM. One of the things I’d like to do is to track the evolution of a function. Has it been modified a lot? How many people has been working on it? Is the function too long? To answer the first question, what we could do is rely on the git log -L:function:file command to give us all the changes a function has suffered.