Load Neo4j Graph Data Science data to DuckDB

Neo4j Graph Data Science provides analytics functionality that operates within the Neo4j database environment and is typically accessed via official drivers or REST-based HTTP APIs. Everything needed to build a working Neo4j Graph Data Science → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Neo4j Graph Data Science to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Neo4j Graph Data Science to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Neo4j Graph Data Science API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Neo4j Graph Data Science API at a glance

These values come from the Neo4j Graph Data Science API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Neo4j Graph Data Science API?

All requests must include an Authorization header using either basic (username:password) or bearer authentication, base64-encoded as specified in RFC 7617.

1. Get your credentials

To obtain Neo4j Aura API credentials: 1. Log in to your Neo4j Aura Console. 2. Navigate to the API credentials or Client credentials section (typically under your account or project settings). 3. Select 'Create client credential' (or similar button). 4. Enter a name for the credential. 5. Copy and save the provided Client ID and Client Secret immediately; they will not be visible again after you close the modal. If your account manages multiple projects, ensure you note the relevant Project ID.

2. Add them to .dlt/secrets.toml

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[sources.neo4j_graph_data_science_source]
auth = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Neo4j Graph Data Science data can I load into DuckDB?

These are the Neo4j Graph Data Science endpoints dlt can load into DuckDB:

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How do I load only new Neo4j Graph Data Science records?

The Neo4j Graph Data Science API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "graph_list", "endpoint": {
    "path": "gds.graph.list",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Neo4j Graph Data Science pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading gds.v2.graph and gds.v2.page_rank from the Neo4j Graph Data Science API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def neo4j_graph_data_science_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<host>:<port>",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": auth},
        },
        "resources": [
            {"name": "graph_list", "endpoint": {"path": "gds.graph.list"}},
            {"name": "graph_stream_relationships", "endpoint": {"path": "gds.graph.relationships.stream"}}
        ],
    }
    yield from rest_api_resources(config)


def load_neo4j_graph_data_science_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="neo4j_graph_data_science_pipeline",
        destination="duckdb",
        dataset_name="neo4j_graph_data_science_data",
    )
    load_info = pipeline.run(neo4j_graph_data_science_source())
    print(load_info)


if __name__ == "__main__":
    load_neo4j_graph_data_science_to_duckdb()

Run it with python neo4j_graph_data_science_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Neo4j Graph Data Science data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("neo4j_graph_data_science_pipeline").dataset()
df = data.graph_list.df()
print(df.head())

SQL:

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SELECT * FROM neo4j_graph_data_science_data.graph_list LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Neo4j Graph Data Science to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Neo4j Graph Data Science loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Neo4j Graph Data Science data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack