Google Blogger Python API Docs | dltHub

Google Blogger API is a RESTful service that lets developers retrieve and manage blog content. The REST API base URL is https://www.googleapis.com/blogger/v3 and Requests can be authorized with an API key or an OAuth 2.0 bearer token..

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 Google Blogger data in under 10 minutes.

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What data can I load from Google Blogger?

Here are some of the endpoints you can load from Google Blogger:

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How do I authenticate with the Google Blogger API?

Public endpoints accept an API key via the key query parameter. Private endpoints require an OAuth 2.0 Bearer token sent in the Authorization: Bearer <token> header.

1. Get your credentials

1. Open the Google Cloud Console (console.cloud.google.com).
2. Create or select a project.
3. Enable the "Blogger API" for the project.
4. In the APIs & Services > Credentials page, click "Create credentials". • Choose "API key" to generate a public API key. • Choose "OAuth client ID" for private access, select Web application, configure authorized redirect URIs, and obtain the client ID and client secret.
5. Copy the generated API key or OAuth client secret for use in dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.google_blogger_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 Google Blogger 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 google_blogger_pipeline.py

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

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Pipeline google_blogger_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_blogger_data
The duckdb destination used duckdb:/google_blogger.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 google_blogger_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 blogs and posts from the Google Blogger 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 google_blogger_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com/blogger/v3",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "blogs", "endpoint": {"path": "users/self/blogs", "data_selector": "items"}},
            {"name": "posts", "endpoint": {"path": "blogs/{blogId}/posts", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_blogger_pipeline",
        destination="duckdb",
        dataset_name="google_blogger_data",
    )
    load_info = pipeline.run(google_blogger_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("google_blogger_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_blogger_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_blogger_pipeline").dataset()
data.posts.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 Google Blogger 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 the API key is missing or invalid, the API returns a 401 Unauthorized error with a message like Invalid Credentials. Ensure the key query parameter is correct or the OAuth token is valid.

Rate limits / quota exceeded

When the request quota is exceeded, the API responds with 403 Forbidden and a message containing quotaExceeded. Reduce request frequency or request a higher quota in the Google Cloud Console.

Pagination issues

List endpoints use nextPageToken to paginate. Omit the token or use an expired token and the API will return the first page without error. Always check for nextPageToken in the response and pass it as the pageToken query parameter for the next request.

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