Graphs answer questions about connections: who is related to whom, what depends on what, which transactions form a ring. Aestus builds and maintains graphs from the systems where the data is created, and brings graph data back to tables and documents for reporting.
How Aestus reads changes from each system, and how it writes them.
As a source: Neo4j change data capture: every node and relationship change, with its labels, keys and before and after state.
As a target: Nodes merged on their key, relationships between them, with the version of the last change on each.
As a source: Neptune Streams, in property-graph form, ordered by commit.
As a target: Nodes and relationships through openCypher, with your keys as ids.
As a source: Apache AGE vertex and edge tables, captured like any PostgreSQL table.
As a target: Vertices and edges through AGE's Cypher.
As a source: Documents that reference each other, captured through change streams.
As a target: Reference arrays or an edges collection, ready for $graphLookup.
As a source: SQL Server node and edge tables, and the tables behind Oracle property graphs.
As a target: Node and edge tables, and the tables an Oracle property graph is defined on.
Between systems of the same kind, and across kinds through a transform.
Rows become nodes; foreign keys and join tables become relationships, with the join table's columns as relationship properties.
Documents become nodes; reference fields and embedded lists become relationships or child nodes.
Each label becomes a table and each relationship type an edge table, or a foreign key when it points to one node.
Nodes become documents, with their relationships embedded or in an edges collection.
Copy and migrate between Neo4j and Neptune, or keep a graph inside your database in step with Neo4j.
Nodes and edge tables in Databricks for graph algorithms next to the rest of your data.
Payments, accounts and devices flow into a graph within seconds, so rings and shared identities are spotted while they happen.
Connect the records of one customer across CRM, orders, support and billing systems.
Purchases and views become relationships that recommendation queries can follow right away.
Give assistants a graph of your products, documents and people that stays current (GraphRAG).
Trace which parts, suppliers, services or systems depend on each other, as the source data changes.
Model who may see what as a graph that follows every change in your directory and applications.
Nodes are identified by your business keys, never by internal ids that differ per database.
A relationship that arrives before its node creates a placeholder, filled in when the node arrives.
Every node and relationship stores the version of its last change.
In two-way setups each label or collection is owned by one side, so changes never loop.
Operational, reporting and analytical SQL databases, from PostgreSQL to the lakehouse.
ExploreDocument, key-value and wide-column stores: MongoDB and its relatives, DynamoDB, Firestore and Cassandra.
ExploreSearch engines and vector stores that serve search, recommendations and AI assistants.
ExploreApplication APIs and the enterprise message brokers: deliver changes into systems and onto event streams, or start a flow from them.
ExploreWant to discuss your use case? Write to [email protected].