Neo4j AuraDB connector

Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See Custom API for the shared SDK pattern and document-upload workflow.

What this connector is forCopy Anchor Link

Managed graph database for nodes, relationships, Cypher queries, and graph models.

Use this guide to choose the resources and row meanings for a Vortexify source integration. Connection ownership belongs to a connector; each dataset gets its own sync, configuration, and extraction behavior.

Data and tables you can pullCopy Anchor Link

The following table families are a starting point for source selection. They describe possible datasets to implement using the vendor reference, not a guarantee that the example implements every table. Actual availability depends on the product edition, deployment, account permissions, and API/schema version.

Relationships and extraction considerationsCopy Anchor Link

These are proposed dataset names, not a universal schema guaranteed by the vendor. Preserve source object IDs and relationship references, and split nested child collections into separate tables when appropriate. Choose the concrete endpoint/report and row meaning from the vendor reference before implementing a sync.

Connecting to Neo4j AuraDBCopy Anchor Link

Use the Aura database URI and database credentials. Query graph data with Cypher and keep node/relationship identities; instance-management credentials are a separate interface.

The example's config keys show the connection fields it uses. Store credentials in platform configuration marked as secret; host/project/database identifiers are ordinary connection settings. Environment and network access must match the selected source.

requirements.inCopy Anchor Link

Declare these packages in the resource's requirements.in. Use the published vortexify package for the SDK and the source-specific driver/client shown below. Choose compatible dependency versions for your runtime; the connector and sync reference explains where each file belongs.

vortexify==0.1.1
pandas>=2,<3
neo4j

Use the Aura database URI and database credentials. Query graph data with Cypher and keep node/relationship identities; instance-management credentials are a separate interface.

Worked example: node query batchesCopy Anchor Link

This source-read pattern shows how to shape the selected resource into DataFrames. Use it inside the dataset sync, retaining shared connection ownership in the connector. Its scope is the named resource and filters; adapt the projection, batching, and preview to the requested table.

Connection factoryCopy Anchor Link

Use this client in the owning connector's client() method and release it in close().

from neo4j import GraphDatabase


def make_client(config: dict):
    return GraphDatabase.driver(
        config["uri"],
        auth=(config["username"], config["password"]),
    )

Source readCopy Anchor Link

import pandas as pd


def read_batches(driver, config):
    with driver.session(
        database=config.get("database", "neo4j"), default_access_mode="READ"
    ) as session:
        result = session.run(
            "MATCH (n) RETURN elementId(n) AS node_id, labels(n) AS labels, properties(n) AS properties"
        )
        batch = []
        for record in result:
            batch.append(record.data())
            if len(batch) == 1000:
                yield pd.json_normalize(batch)
                batch = []
        if batch:
            yield pd.json_normalize(batch)

What to adaptCopy Anchor Link

This is a node scan starting point. Use a narrower label/property projection for a business table and extract relationships separately. elementId is an extraction identity with transaction/lifecycle caveats; retain stable business keys for cross-run joins. Preview uses a separate read with LIMIT 100. Close the driver.

Use the custom API SDK starting point to connect the read operation to Connector and Sync. For a source with its own driver, retain that client in Connector.client() and replace the HTTP request with the driver read. An extract(*, avg_row_size_bytes=None) implementation yields bounded DataFrames. An independent preview() uses a fresh connector and a small read; it must not call extraction or advance real state.

Choose explicit output columns and stable IDs for the requested table. The examples use json_normalize where useful to illustrate row shaping; nested arrays still need deliberate child tables and a stable declared output schema. A first-page sample is useful for development, while a full sync must follow all relevant continuation/parent-resource paths. Design incremental watermarks, update/deletion behavior, and rate-limit handling for the actual source before enabling incremental mode.

The purpose is to give a coding agent a concrete pattern to adapt, including for similar sources. These snippets are examples rather than a claim of complete vendor coverage. Check the source operation reference and representative responses before running with a customer's account; live vendor execution has not been verified here.

Sources and further readingCopy Anchor Link

• Vendor connection and resource reference — The interface/access prerequisites underlying this connection path; follow the selected resource's documentation for fields, queries, and pagination.
• Vortexify connectors and syncs — Connector ownership, schemas, preview isolation, extraction, and checkpoint contracts.
• Vortexify CLI workflow — Registering resources, committing source, and providing credential configuration.