Cognix SEO Python API Docs | dltHub

Cognix SEO is an SEO analytics platform exposing keyword rankings, technical SEO scans, backlink analysis, content performance, and competitor analysis via a REST API. The REST API base URL is https://api.cognix.au/v1/seo and all requests require a Bearer token for authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Cognix SEO data in under 10 minutes.

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What data can I load from Cognix SEO?

Here are some of the endpoints you can load from Cognix SEO:

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How do I authenticate with the Cognix SEO API?

Authentication uses a Bearer token. Include header: Authorization: Bearer YOUR_API_KEY. Obtain API key from Cognix support/dashboard.

1. Get your credentials

1. Sign in to your Cognix account at the Cognix dashboard. 2) Navigate to Account Settings or API Keys. 3) Create or request a new API key for the SEO Analytics product. 4) Copy the generated key and store it securely; use it as a Bearer token in Authorization header. If no key option in dashboard, contact Cognix support via https://cognix.au/contact-us.

2. Add them to .dlt/secrets.toml

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[sources.cognix_seo_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Cognix SEO API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python cognix_seo_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline cognix_seo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cognix_seo_data
The duckdb destination used duckdb:/cognix_seo.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline cognix_seo_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads keyword_rankings and technical_audit from the Cognix SEO API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def cognix_seo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cognix.au/v1/seo",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "keyword_rankings", "endpoint": {"path": "rankings", "data_selector": "data.rankings"}},
            {"name": "technical_audit", "endpoint": {"path": "technical", "data_selector": "data.issues"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cognix_seo_pipeline",
        destination="duckdb",
        dataset_name="cognix_seo_data",
    )
    load_info = pipeline.run(cognix_seo_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("cognix_seo_pipeline").dataset()
sessions_df = data.keyword_rankings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cognix_seo_data.keyword_rankings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cognix_seo_pipeline").dataset()
data.keyword_rankings.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Cognix SEO data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized or error code MISSING_API_KEY/INVALID_API_KEY, verify the Authorization header uses: Authorization: Bearer YOUR_API_KEY. If the dashboard does not show keys, contact Cognix support.

Rate limits and 429 responses

The API returns 429 Too Many Requests when quotas are exceeded. Implement exponential backoff and retries. Log request_id from error responses for support.

Pagination

List endpoints return arrays inside a response envelope under the data key (e.g. data.rankings, data.backlinks). Use query params like page and per_page when present; if not, iterate using cursors from response (check data.next_cursor/meta fields).

Common error payload

Errors are returned with structure: {"status":"error","error":{"code":"ERROR_CODE","message":"...","details":{...}},"request_id":"..."}. Use request_id when contacting support.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack