This is the companion repo for the "Continuous Visibility into Ephemeral Cloud Environments" series.

The file queries/queries.json contains a set of custom queries specifically created for analysing data collected by Cartography, and is structured as a list of dictionaries, where each dictionary represents an annotated query (enriched with metadata).

The consumers/elasticsearch folder contains all the code needed to get you started with integrating Elasticsearch with Cartography data.

For more information, please refer to the README.md file in that folder, and the "Tracking Moving Clouds: How to continuously track cloud assets with Cartography" blog post.

The consumers/jupyter_notebooks folder contains all the code needed to get you started with your own Jupyter reports for analysing Cartography data.

For more information, please refer to the README.md file in that folder, and the "Repeatability: Jupyter Notebooks" section of the "Mapping Moving Clouds: how to stay on top of your ephemeral environments with Cartography" blog post.

I've now described an automated process to get Neo4J and Cartography up and running in a Kubernetes cluster at: Automating Cartography Deployments on Kubernetes.

• [CODE] Cartography's source code
• [CODE] Cartography Deployment on Kubernetes
• [CODE] Terraform AWS Security Reviewer
• [BLOG] Mapping Moving Clouds: How to stay on top of your ephemeral environments with Cartography
• [BLOG] Tracking Moving Clouds: How to continuously track cloud assets with Cartography
• [BLOG] Automating Cartography Deployments on Kubernetes
• [BLOG] Cross Account Auditing in AWS and GCP
• [BLOG] Kubernetes Lab on Baremetal
• [TALK] Cartography: using graphs to improve and scale security decision-making