Pyze Python API Docs | dltHub

Pyze is a SaaS usage analytics and customer intelligence platform for mobile, web and chat applications. The REST API base URL is https://import.pyze.com and Requests use an appKey credential provided by Pyze (included in request body for import endpoints); full API credentials/endpoints are provided by your Pyze account manager..

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 Pyze data in under 10 minutes.

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What data can I load from Pyze?

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

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How do I authenticate with the Pyze API?

Enterprise import APIs accept an appKey value (example param name appKey) in the JSON payload. Account-level upload endpoints and credentials are provided by your Pyze account manager; Content-Type: application/json is required for JSON requests.

1. Get your credentials

1. Contact your Pyze account manager or Pyze support; 2) Request Enterprise Import API access and an upload endpoint and credentials (appKey and any access restrictions); 3) Receive your tenant-specific import endpoint (example: https://import.pyze.com/users/profile) and appKey; 4) Store the appKey securely and include it in requests as documented.

2. Add them to .dlt/secrets.toml

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[sources.pyze_source]
app_key = "your_app_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 Pyze 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 pyze_pipeline.py

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

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Pipeline pyze_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pyze_data
The duckdb destination used duckdb:/pyze.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 pyze_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 events and users_profile from the Pyze 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 pyze_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://import.pyze.com",
            "auth": {
                "type": "api_key",
                "appKey": app_key,
            },
        },
        "resources": [
            {"name": "users_profile", "endpoint": {"path": "users/profile", "data_selector": "user_profiles"}},
            {"name": "events", "endpoint": {"path": "v1/events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pyze_pipeline",
        destination="duckdb",
        dataset_name="pyze_data",
    )
    load_info = pipeline.run(pyze_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("pyze_pipeline").dataset()
sessions_df = data.users_profile.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pyze_data.users_profile LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pyze_pipeline").dataset()
data.users_profile.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 Pyze 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 or 403 responses, verify you are using the correct appKey for the target import endpoint and that your account has import API access. Contact your Pyze account manager to confirm permissions and tenant endpoint.

Invalid payloads and validation errors

The events API returns 400 for invalid input (missing required fields). Ensure required fields (e.g., type, event, userId, eventTime, appKey for events or user_profiles array for profiles) are present and valid.

Accepted vs processed

The events import endpoint returns 202 when the event is accepted for processing; acceptance does not guarantee processing successmonitor Pyze side processing if provided.

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