AWS Server-less data pipelines with Terraform

Code supporting Blog post series AWS Server-less data pipelines with Terraform.

Setup

Python setup

Though not a requirement, we recommend using Anaconda to manage python dependencies. Here are the steps that we will walk you though:

Setup Anaconda.
Setup conda environment
Setup local env

Setup Anaconda

To download Anaconda package manager, go to: https://www.continuum.io/downloads.

After installing locally the conda environment, proceed to setup this project environment.

Setup local conda environment

For dependency management we are using conda-requirements.txt and requirements.txt. Please "cd" into the current reposotory and build your conda environment based on those conda-requirements and requirements:

conda create -n analytics python=3.6
source activate analytics
conda install --file conda_requirements.txt
pip install -r pip_requirements.txt

To deactivate this specific virtual environment:

source deactivate

If you need to completely remove this conda env, you can use the following command:

conda env remove --name analytics

Terraform setup

Install Terraform

brew install terraform

In order to automatic format terraform code (and have it cleaner), we use pre-commit hook. To install pre-commit.
Run pre-commit install to setup locally hook for terraform code cleanup.

pre-commit install

Planning & applying changes

In order to automate and simplify the provisioning steps, we provide a Makefile, which can be used for:

deploy whole Terraform setup by simply running "make apply";
build and deploy individual Lambda function(s);

Before running any 'make' command, we recommend exporting the specific environment you want to use. For example:

export ENVIRONMENT=dev

In order to safely view the executions steps that will be executed, it is a best practice running a "terraform plan" You can do so via the make file which we provide at the root level of this directory, simply by running the following command:

make plan

To apply Terraform changes, use:

make apply

Authors/Contributors

See the list of contributors who participated in this project.

diogoaurelio/serverless-pipelines-blog-series