Load Canva data to DuckDB

Canva Connect API is a REST API for building integrations and managing resources within the Canva ecosystem. Everything needed to build a working Canva → 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.

---

Build your Canva 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 Canva 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 Canva 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.

---

Canva API at a glance

These values come from the Canva API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Canva API?

Requests to the Canva Connect API require an 'Authorization: Bearer {token}' header. OAuth 2.0 access tokens are obtained using the OAuth 2.0 Authorization Code flow with PKCE.

1. Get your credentials

1. Log in to your account on the Canva Developer Portal (https://www.canva.com/developers/integrations/). Ensure Multi-Factor Authentication (MFA) is enabled on your Canva account.
2. Click 'Create an integration' to start a new app project.
3. Under the 'Configuration' section, enter an 'Integration name'.
4. Locate your 'Client ID' and make a note of it.
5. Click 'Generate secret' to create a 'Client secret'. Save this value immediately in a secure location, as it cannot be retrieved again later.
6. Configure your 'Authentication' settings, including adding at least one authorized redirect URL (e.g., http://127.0.0.1:3001/oauth/redirect for local development).
7. Ensure you select the appropriate OAuth scopes required by your integration in the 'Scopes' configuration tab.

2. Add them to .dlt/secrets.toml

Copy
[sources.canva_source]
canva_client_id = "your_client_id_here"
canva_client_secret = "your_client_secret_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.

---

What Canva data can I load into DuckDB?

These are the Canva endpoints dlt can load into DuckDB:

---

How do I load only new Canva records?

Canva exposes continuation on v1/designs, 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.

Copy
{"name": "designs", "endpoint": {
    "path": "v1/designs",
    "data_selector": "items",
    "incremental": {"cursor_path": "continuation", "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.

---

What does the generated Canva pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/oauth/token (used to generate access tokens) and /v1/asset-uploads (used for initiating asset upload jobs). from the Canva API into DuckDB:

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

@dlt.source
def canva_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.canva.com/rest",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "designs", "endpoint": {"path": "v1/designs", "data_selector": "items"}},
            {"name": "brand_templates", "endpoint": {"path": "v1/brand-templates", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_canva_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="canva_pipeline",
        destination="duckdb",
        dataset_name="canva_data",
    )
    load_info = pipeline.run(canva_source())
    print(load_info)


if __name__ == "__main__":
    load_canva_to_duckdb()

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

---

How do I query Canva 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):

Copy
import dlt

data = dlt.pipeline("canva_pipeline").dataset()
df = data.designs.df()
print(df.head())

SQL:

Copy
SELECT * FROM canva_data.designs LIMIT 10;

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

---

How do I deploy the Canva 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 Canva loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Canva 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.

---

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