Load Getty Images data to DuckDB

Getty Images provides an API for accessing media assets, metadata, and licensing tools for professional content creators and developers. Everything needed to build a working Getty Images → 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 Getty Images 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 Getty Images 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 Getty Images 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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Getty Images API at a glance

These values come from the Getty Images API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Getty Images API?

Getty Images requires an 'Api-Key' header for all requests and an optional 'Authorization' header using the 'Bearer' scheme for requests requiring elevated privileges.

1. Get your credentials

Getty Images does not provide a self-service developer portal for generating API keys. To obtain API credentials, you must contact a Getty Images account representative to discuss licensing and API access requirements. Once approved and connected to your license agreement, they will provide you with your API Key (client_id) and API Secret (client_secret).

2. Add them to .dlt/secrets.toml

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[sources.getty_images_source]
api_key = "your_api_key_here"
api_secret = "your_api_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.

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What Getty Images data can I load into DuckDB?

These are the Getty Images endpoints dlt can load into DuckDB:

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How do I load only new Getty Images records?

The Getty Images 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": "search_images", "endpoint": {
    "path": "v3/search/images",
    # 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 Getty Images pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading search/images and oauth2/token from the Getty Images API into DuckDB:

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

@dlt.source
def getty_images_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.gettyimages.com/v3",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "search_images", "endpoint": {"path": "v3/search/images", "data_selector": "images"}},
            {"name": "search_images_creative", "endpoint": {"path": "v3/search/images/creative", "data_selector": "images"}}
        ],
    }
    yield from rest_api_resources(config)


def load_getty_images_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="getty_images_pipeline",
        destination="duckdb",
        dataset_name="getty_images_data",
    )
    load_info = pipeline.run(getty_images_source())
    print(load_info)


if __name__ == "__main__":
    load_getty_images_to_duckdb()

Run it with python getty_images_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 Getty Images 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("getty_images_pipeline").dataset()
df = data.search_images.df()
print(df.head())

SQL:

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SELECT * FROM getty_images_data.search_images LIMIT 10;

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

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How do I deploy the Getty Images 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 Getty Images 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 Getty Images 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