cloudquery/cloudquery
Cloudquery
CloudQuery is a cloud infrastructure ETL tool and multi-cloud data pipeline designed to collect, synchronize, and normalize resource metadata from various cloud providers and SaaS platforms. It functions as a centralized asset inventory manager and security posture manager, extracting configuration and state data into relational databases, data lakes, or data warehouses.
The system distinguishes itself by transforming complex, nested cloud API responses into flat relational tables, enabling the use of standard SQL for asset querying and analysis. It employs a modular plugin system for data extraction and driver-based adapters for destination-agnostic loading, allowing metadata to be pushed into diverse storage backends.
The platform covers several broad capability areas, including cloud security posture management, FinOps cost optimization, and infrastructure compliance auditing. It utilizes SQL-based transformation pipelines to implement security frameworks, detect configuration drift, and identify underutilized resources. Additionally, the tool provides event-driven responses to fire webhooks or alerts when policy violations occur.
Features
- Cloud Provider Metadata Extraction - Pulls infrastructure and service data from cloud providers, SaaS applications, and databases for centralized analysis.
- Cloud Asset Inventory Managers - Queries cloud provider APIs to retrieve and display consolidated lists of infrastructure assets in a centralized registry.
- Cloud Metadata Transformations - Applies pre-built transformation projects to raw cloud metadata to implement security frameworks or asset inventories.
- Relational Data Transformations - Maps nested cloud API responses into flat relational tables for consistent SQL-based querying.
- API-to-Relational Mappers - Transforms complex, nested cloud API responses into flat relational tables to enable analysis using standard SQL.
- Cloud Metadata Ingestors - Ingests technical and operational metadata from cloud services into databases and data lakes for long-term storage.
- SQL Data Loading and Transformation - Processes raw ingested metadata into structured security and compliance reports using SQL as the primary transformation logic.
- SQL Infrastructure Querying - Analyzes infrastructure state using SQL to identify resource configurations, missing tags, and cost patterns.
- Metadata Synchronizers - Automates the synchronization of resource configurations from multiple cloud providers into a centralized destination.
- Cloud Infrastructure Data Collection - Provides programmatic collection of resource metadata from cloud provider APIs for centralized infrastructure analysis.
- Metadata ETL Pipelines - Provides a data pipeline that extracts resource metadata from cloud providers and SaaS sources into relational databases.
- Azure Resource Extraction - Retrieves configuration and state data from Microsoft Azure services and loads it into a destination for analysis.
- GCP Resource Extraction - Retrieves configuration data from Google Cloud Platform APIs and loads it into a specified data destination.
- Multi-Cloud Data Aggregation - Retrieves resource information from multiple cloud providers and aggregates it into a structured data store for analysis.
- Cloud Security Posture Management - Identifies misconfigured cloud resources and security vulnerabilities to manage and improve the overall security posture.
- Data Destination Connectors - Implements driver-based adapters to establish connections and push metadata into various target storage systems and databases.
- Data Warehouse Exporters - Transfers collected cloud metadata into databases, data lakes, or data warehouses for long-term storage and querying.
- Operational Signal Exports - Sends synchronized cloud data to external analysis tools and alerting systems to create actionable operational signals.
- Cloud Infrastructure Cost Optimization - Identifies underutilized or orphaned cloud resources to reduce unnecessary operational spending.
- Security Posture Dashboards - Provides customizable dashboards for monitoring security posture and infrastructure health based on synchronized data.
- Event-Driven Workflow Triggers - Initiates automated workflows, webhooks, or alerts based on detected configuration drift or policy violations in synchronized data.
- Cloud Cost Analytics - Extracts resource cost and usage data to identify savings opportunities and underutilized assets.
- Compliance Frameworks - Transforms raw cloud metadata into structured security reports based on industry-standard compliance frameworks.
- Audit and Compliance - Verifies organizational adherence to security policies and regulatory standards using SQL-based guardrails.
- Resource Compliance Monitoring - Continuously tracks cloud resource configurations against policy definitions to detect untagged assets and unauthorized regional deployments.
- Infrastructure Policy Enforcement - Defines SQL-based guardrails to evaluate live infrastructure against security or compliance rules.
- Configuration-Driven Pipelines - Employs pre-defined configuration schemas to automate the setup of data flows between specific sources and destinations.
- Plugin-Based Architectures - Utilizes a modular plugin system to fetch raw metadata from diverse cloud APIs and SaaS platforms via standardized interfaces.
- Data Integration - High-performance ELT framework with pluggable architecture.
- Query Engines - Extracts and loads cloud assets into normalized PostgreSQL tables.
- AWS Security - Framework for real-time cloud data analysis and alerting.
- Cloud Security - Transforms cloud infrastructure into queryable SQL tables.
- Continuous Security Monitoring - Exposes cloud configuration as SQL for security analysis.
- Cybersecurity - Audits and evaluates configurations of cloud assets.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
