Load Gravity Forms data to BigQuery

Gravity Forms REST API is a WordPress-hosted interface for managing forms, entries, feeds, fields, notifications, and submissions. Everything needed to build a working Gravity Forms → BigQuery 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 Gravity Forms to BigQuery 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 Gravity Forms 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.

---

Gravity Forms API at a glance

These values come from the Gravity Forms API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Gravity Forms API?

The API supports Basic Authentication, which requires an Authorization header with a base64-encoded string of the consumer key and consumer secret in the format 'Basic base64(consumer_key

)'. OAuth 1.0a is also supported.

1. Get your credentials

1. Log in to your WordPress dashboard. 2. Navigate to Forms > Settings. 3. Click on the REST API tab. 4. Check the box to Enable access to the API. 5. In the Authentication section, click Add Key. 6. Provide a description, select an authorized user, and set the appropriate permissions (Read/Write). 7. Copy the generated Consumer Key and Consumer Secret immediately, as they will not be displayed again. 8. Click Update to save the settings.

2. Add them to .dlt/secrets.toml

Copy
[sources.gravity_forms_source]
consumer_key = "ck_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
consumer_secret = "cs_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

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 Gravity Forms data can I load into BigQuery?

These are the Gravity Forms endpoints dlt can load into BigQuery:

---

How do I load only new Gravity Forms records?

Gravity Forms exposes paging[current_page] on gf/v2/forms, 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": "forms", "endpoint": {
    "path": "gf/v2/forms",
    "incremental": {"cursor_path": "paging[current_page]", "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 Gravity Forms pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /forms and /entries from the Gravity Forms API into BigQuery:

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

@dlt.source
def gravity_forms_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://your-wordpress-site.example/wp-json/gf/v2",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "forms", "endpoint": {"path": "gf/v2/forms"}},
            {"name": "entries", "endpoint": {"path": "gf/v2/entries"}}
        ],
    }
    yield from rest_api_resources(config)


def load_gravity_forms_to_bigquery() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gravity_forms_pipeline",
        destination="bigquery",
        dataset_name="gravity_forms_data",
    )
    load_info = pipeline.run(gravity_forms_source())
    print(load_info)


if __name__ == "__main__":
    load_gravity_forms_to_bigquery()

Run it with uv run python gravity_forms_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 Gravity Forms data in BigQuery?

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("gravity_forms_pipeline").dataset()
df = data.entries.df()
print(df.head())

SQL:

Copy
SELECT * FROM gravity_forms_data.entries LIMIT 10;

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

---

How do I deploy the Gravity Forms to BigQuery 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 Gravity Forms 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 Gravity Forms 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