Load VictoriaMetrics Cloud data to DuckDB

VictoriaMetrics Cloud provides an API for managing cloud resources such as deployments, access tokens, and infrastructure configuration. Everything needed to build a working VictoriaMetrics Cloud → 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 VictoriaMetrics Cloud 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 VictoriaMetrics Cloud 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 VictoriaMetrics Cloud 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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VictoriaMetrics Cloud API at a glance

These values come from the VictoriaMetrics Cloud API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the VictoriaMetrics Cloud API?

The API uses a custom header 'X-VM-Cloud-Access' for authentication with the generated API key. Requests to individual deployment endpoints for data operations use the 'Authorization: Bearer ' header instead.

1. Get your credentials

To obtain API credentials for the VictoriaMetrics Cloud REST API, follow these steps: 1. Log in to the VictoriaMetrics Cloud console. 2. Navigate to the API Keys section (typically found in Account Management or Organizations settings). 3. Click the option to create a new API key. 4. Provide a name for the key, set its expiration (Lifetime), select the required permissions (Read, Write, or both), and grant access to specific or all deployments as needed. 5. Save/Generate the key. Ensure you copy the generated API key value immediately, as it is a sensitive secret used for authentication.

2. Add them to .dlt/secrets.toml

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[sources.victoriametrics_cloud_source]
vm_cloud_api_key = "your_api_key_here"

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 VictoriaMetrics Cloud data can I load into DuckDB?

These are the VictoriaMetrics Cloud endpoints dlt can load into DuckDB:

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How do I load only new VictoriaMetrics Cloud records?

VictoriaMetrics Cloud exposes id on api/v1/deployments, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "deployments", "endpoint": {
    "path": "api/v1/deployments",
    "data_selector": "deployments",
    "incremental": {"cursor_path": "id", "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 VictoriaMetrics Cloud pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading deployments and access-tokens from the VictoriaMetrics Cloud API into DuckDB:

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

@dlt.source
def victoriametrics_cloud_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.victoriametrics.cloud",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "deployments", "endpoint": {"path": "api/v1/deployments", "data_selector": "deployments"}},
            {"name": "access_tokens", "endpoint": {"path": "api/v1/access_tokens", "data_selector": "access_tokens"}}
        ],
    }
    yield from rest_api_resources(config)


def load_victoriametrics_cloud_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="victoriametrics_cloud_pipeline",
        destination="duckdb",
        dataset_name="victoriametrics_cloud_data",
    )
    load_info = pipeline.run(victoriametrics_cloud_source())
    print(load_info)


if __name__ == "__main__":
    load_victoriametrics_cloud_to_duckdb()

Run it with python victoriametrics_cloud_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 VictoriaMetrics Cloud 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("victoriametrics_cloud_pipeline").dataset()
df = data.deployments.df()
print(df.head())

SQL:

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SELECT * FROM victoriametrics_cloud_data.deployments LIMIT 10;

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

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How do I deploy the VictoriaMetrics Cloud 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 VictoriaMetrics Cloud 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 VictoriaMetrics Cloud 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