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Fix Inventory Documentation

website·Indexed 32 days ago

https://fixinventory.org

Documentation for Fix Inventory version 4.2.0, featuring a Unified Data Model for cloud resources across AWS, Azure, GCP, and DigitalOcean. Includes references for the CLI, Fix Inventory Shell (fixsh), Notebook library, IAM permissions, security configurations, and telemetry.

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What's inside Fix Inventory

  1. Overview of the aws_eks_cluster resource

    The aws_eks_cluster resource represents an AWS EKS Cluster, a managed Kubernetes service on Amazon Web Services. It is used to automate the deployment, scaling, and management of the Kubernetes control plane and worker nodes, integrating with AWS networking, storage, and security services to run distributed applications across multiple availability zones.

  2. Overview of the azure_key_vault resource

    The azure_key_vault resource represents an Azure Key Vault, a cloud service used for the secure storage and management of cryptographic keys, secrets, and certificates. It provides access control for sensitive information, encryption, key rotation, and monitoring to ensure security compliance in cloud environments. It is categorized under Management and belongs to the key-vault service.

  3. Overview of Fix Inventory Shell (fixsh)

    The fixsh command launches the Fix Inventory Shell, a tool used to interact with fixcore. It enables developers to explore graphs, identify resources for cleanup, correct tagging, aggregate metadata for metrics, and format output for integration with third-party scripts or systems.

  5. Overview of the aws_ecr_repository resource

    The aws_ecr_repository resource represents an AWS Elastic Container Registry (ECR) repository. ECR is a managed Docker container registry service used to store, manage, and deploy container images. It provides secure access control, integrates with other AWS services, and supports vulnerability scanning for images.

  7. Overview of AWS SageMaker Auto ML Job

    The aws_sagemaker_auto_ml_job resource represents an AWS SageMaker Auto ML Job, which automates the machine learning model development lifecycle. It automatically handles data preprocessing, algorithm selection, and hyperparameter tuning. To use it, a user provides a dataset and specifies a target variable; the system then trains and evaluates multiple models to deliver the best-performing one.

  8. Overview of Azure Network Web Application Firewall Policy

    The azure_network_web_application_firewall_policy is a security configuration used to protect Azure web applications from common vulnerabilities and threats. It functions by filtering incoming traffic, blocking malicious requests, and enforcing security measures. This policy can be applied to the following Azure services: Azure Application Gateway, Azure Front Door, and Azure CDN.

  9. Overview of cleanup-aws-vpcs Infrastructure App

    The cleanup-aws-vpcs Infrastructure App automatically marks network resource dependencies for cleanup when a VPC is marked for cleanup. It handles the following AWS resources:

    • VPC Peering Connections
    • EC2 Network ACLs
    • EC2 Network Interfaces
    • ELB and ALB
    • ALB Target Groups
    • EC2 Subnets
    • EC2 Security Groups
    • EC2 Internet Gateways
    • EC2 NAT Gateways
    • EC2 Route Tables

    Note: Resources tagged with expiration: never will not be flagged for cleanup.

  10. Overview of Azure Network Application Gateway (`azure_network_application_gateway`)

    The azure_network_application_gateway is a web traffic load balancer operating at the application layer (Layer 7). It is used to route incoming requests to backend pools based on criteria such as URL paths and host headers. Key capabilities include SSL termination, session affinity, and Web Application Firewall (WAF) protection for both HTTP and HTTPS traffic.

  11. Overview of the aws_sagemaker_domain resource

    The aws_sagemaker_domain resource represents an AWS SageMaker Domain, which is a managed environment for machine learning workflows. It provides a unified interface for data scientists and developers to access SageMaker tools, including notebooks, data preparation, model training, and deployment. It enables collaboration on projects, resource sharing, and centralized access control management for the end-to-end ML process on AWS.

  12. Overview of Azure Cosmos DB Gremlin Database

    The azure_cosmos_db_gremlin_database is a graph database service provided by Microsoft Azure. It is designed for storing and querying graph data using the Apache TinkerPop Gremlin API, allowing developers to model complex relationships between entities, perform graph traversals, and execute graph operations. Key capabilities include global distribution and multi-region writes for graph data workloads.