4.4 Databases and Storage

4.4 Databases and Storage

ChordianAI integrates with multiple data storage paradigms, allowing it to operate seamlessly across structured, unstructured, and semi-structured environments. This ensures analytical, forecasting, optimization, and knowledge workflows always have access to the correct data model.

ChordianAI integrates with multiple data storage paradigms, allowing it to operate seamlessly across structured, unstructured, and semi-structured environments. This ensures analytical, forecasting, optimization, and knowledge workflows always have access to the correct data model.

SQL Engines
Full support for relational databases and warehouses:
• PostgreSQL, MySQL
• SQL Server, Oracle SQL
• Snowflake, BigQuery (via connectors)

NoSQL Systems
Integration with NoSQL engines for semi-structured datasets, logs, and metadata:
• MongoDB
• DynamoDB
• Firestore
• Cosmos DB

Object Storage
Native compatibility with cloud object stores for:
• documents
• logs
• unstructured archives
• large analytical exports
• machine learning datasets

Graph Databases
First-class integration with graph engines powering knowledge-graph workflows, dependency analysis, and relationship modeling:
• Neo4j
• AWS Neptune
• TigerGraph

These storage integrations enable ChordianAI to unify data across every business function, ensuring that workflows have consistent access to accurate, governed, and context-rich datasets.

ChordianAI

Change the way you run your business with Chordian AI. Sign up now.

ChordianAI

Change the way you run your business with Chordian AI. Sign up now.