Load LinkedIn Advertising data to Snowflake

The LinkedIn Marketing Solutions API allows developers to programmatically manage advertising campaigns, ad accounts, and marketing assets on LinkedIn. Everything needed to build a working LinkedIn Advertising → Snowflake 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 LinkedIn Advertising to Snowflake pipeline

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

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 LinkedIn Advertising 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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LinkedIn Advertising API at a glance

These values come from the LinkedIn Advertising API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the LinkedIn Advertising API?

All requests require an 'Authorization' header with a Bearer token. Additionally, a 'Linkedin-Version' header (format YYYYMM) is required for all versioned API calls.

1. Get your credentials

To access the LinkedIn Marketing/Advertising API, you must first register an application in the LinkedIn Developer Portal. Navigate to 'My Apps' and click 'Create App'. In the 'Products' tab of your app settings, select and apply for the 'Advertising API'. Once your application is approved by LinkedIn, you will obtain a 'Client ID' and 'Client Secret' under the 'Auth' tab of your app dashboard. Note that these credentials alone do not grant access to data; you must implement the 3-legged OAuth 2.0 flow, where an authenticated user (with appropriate ad account roles) grants consent to your application to generate an access token.

2. Add them to .dlt/secrets.toml

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[sources.linkedin_advertising_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
access_token = "your_access_token_here"
refresh_token = "your_refresh_token_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 LinkedIn Advertising data can I load into Snowflake?

These are the LinkedIn Advertising endpoints dlt can load into Snowflake:

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How do I load only new LinkedIn Advertising records?

LinkedIn Advertising exposes N/A on adCampaigns, 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": "ad_campaigns", "endpoint": {
    "path": "adCampaigns",
    "data_selector": "elements",
    "incremental": {"cursor_path": "N/A", "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 LinkedIn Advertising pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading adAccounts and adCampaigns from the LinkedIn Advertising API into Snowflake:

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

@dlt.source
def linkedin_advertising_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.linkedin.com/rest/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "ad_campaigns", "endpoint": {"path": "adCampaigns", "data_selector": "elements"}},
            {"name": "ad_creatives", "endpoint": {"path": "adCreatives", "data_selector": "elements"}}
        ],
    }
    yield from rest_api_resources(config)


def load_linkedin_advertising_to_snowflake() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="linkedin_advertising_pipeline",
        destination="snowflake",
        dataset_name="linkedin_advertising_data",
    )
    load_info = pipeline.run(linkedin_advertising_source())
    print(load_info)


if __name__ == "__main__":
    load_linkedin_advertising_to_snowflake()

Run it with uv run python linkedin_advertising_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 LinkedIn Advertising data in Snowflake?

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("linkedin_advertising_pipeline").dataset()
df = data.ad_campaigns.df()
print(df.head())

SQL:

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SELECT * FROM linkedin_advertising_data.ad_campaigns LIMIT 10;

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

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How do I deploy the LinkedIn Advertising to Snowflake 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 LinkedIn Advertising 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 LinkedIn Advertising 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