Braze Python API Docs | dltHub

Braze is a customer engagement platform that provides REST APIs to track users, send messages, export analytics, and manage campaign and content resources. The REST API base URL is Use the REST endpoint specific to your account (examples): https://rest.iad-01.braze.com, https://rest.iad-02.braze.com, https://rest.fra-01.braze.eu (choose the REST endpoint shown with your API key in the Braze dashboard). and all requests require a REST API key provided as a Bearer token in the Authorization header..

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 Braze data in under 10 minutes.

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What data can I load from Braze?

Here are some of the endpoints you can load from Braze:

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How do I authenticate with the Braze API?

Braze uses REST API keys sent in the Authorization header as a Bearer token (Authorization: Bearer YOUR_REST_API_KEY); each API key is scoped to permissions and may be IP-allowlisted.

1. Get your credentials

1. In Braze dashboard go to Settings > APIs and Identifiers (or APIs/Integrations) 2) Click Create API Key, name the key and select required permissions 3) (Optional) set allowed IPs; create the key 4) Copy the REST Endpoint shown on the API Key details and the API key (store securely).

2. Add them to .dlt/secrets.toml

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[sources.braze_source]
api_key = "your_braze_rest_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 Braze 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 braze_pipeline.py

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

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Pipeline braze_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset braze_data
The duckdb destination used duckdb:/braze.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 braze_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 segments and campaigns from the Braze 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 braze_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Use the REST endpoint specific to your account (examples): https://rest.iad-01.braze.com, https://rest.iad-02.braze.com, https://rest.fra-01.braze.eu (choose the REST endpoint shown with your API key in the Braze dashboard).",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "segments", "endpoint": {"path": "segments/list", "data_selector": "segments"}},
            {"name": "campaigns", "endpoint": {"path": "campaigns/list", "data_selector": "campaigns"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="braze_pipeline",
        destination="duckdb",
        dataset_name="braze_data",
    )
    load_info = pipeline.run(braze_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("braze_pipeline").dataset()
sessions_df = data.segments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM braze_data.segments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("braze_pipeline").dataset()
data.segments.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 Braze 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 403 Forbidden, verify your Authorization header uses "Bearer <REST_API_KEY>", the key is active, and that the key has the permission required for the endpoint. Check IP allowlisting on the key.

Rate limits

Braze applies account/endpoint rate limits (default: 250,000 requests/hour for most APIs). Some high-volume endpoints have dedicated limits; if you hit rate limits you will receive 429 responses—back off and retry with exponential backoff.

Export and pagination

Many export endpoints operate as POST export jobs (not direct GETs). List endpoints return paginated results; use the documented query parameters and follow job status fields for export endpoints. For very large exports, use the export job endpoints and poll or cancel via /export/* endpoints.

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