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Loading Clubhouse Data to BigQuery with dlt in Python

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Loading data from Clubhouse, a social media audio app that offers real-time virtual rooms for audio communication, to BigQuery, a serverless and cost-effective enterprise data warehouse, is straightforward with the open-source Python library dlt. This guide will walk you through the steps needed to set up and execute this data pipeline. By leveraging dlt, you can efficiently manage and scale your data loading process across clouds. For more information about Clubhouse, visit their website.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data lineage. Learn more.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Learn more.
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes, allowing necessary actions for data integrity. Learn more.
  • Scalability and Fine-tuning: dlt offers mechanisms to scale up and fine-tune pipelines, including parallel processing and memory buffer adjustments. Learn more.
  • Securely Handling Secrets: dlt provides methods to securely handle secrets, ensuring safe management of sensitive information. Learn more.

Getting started with your pipeline locally​

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

0. Prerequisites​

dlt and dlt-init-openapi requires Python 3.9 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt and dlt-init-openapi​

First you need to install the dlt-init-openapi cli tool.

pip install dlt-init-openapi

The dlt-init-openapi cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.

# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you 
# can just hit Enter and all will be rendered.
dlt-init-openapi clubhouse --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/clubhouse_api.yaml --global-limit 2
cd clubhouse_pipeline
# install generated requirements
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt>=0.4.12

You now have the following folder structure in your project:

clubhouse_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                # The rest api verified source
│   └── ...
├── clubhouse/                
│   └── __init__.py          # TODO: possibly tweak this file
├── clubhouse_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

1.1. Tweak clubhouse/__init__.py​

This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api source set up in a different way. The generated file for the clubhouse source will look like this:

Click to view full file (133 lines)

from typing import List

import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig


@dlt.source(name="clubhouse_source", max_table_nesting=2)
def clubhouse_source(
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
            "paginator": {
                "type":
                    "page_number",
                "page_param":
                    "page",
                "total_path":
                    "",
                "maximum_page":
                    20,
            },
        },
        "resources": 
        [
        {
            "name": "check_for_update",
            "table_name": "check_for_update",
            "endpoint": {
                "path": "/check_for_update",
                "params": {
                    # the parameters below can optionally be configured
                    # "is_testflight": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_actionable_notification",
            "table_name": "get_actionable_notification",
            "endpoint": {
                "path": "/get_actionable_notifications",
            }
        },
        {
            "name": "get_all_topic",
            "table_name": "get_all_topic",
            "endpoint": {
                "path": "/get_all_topics",
            }
        },
        {
            "name": "get_channel",
            "table_name": "get_channel",
            "endpoint": {
                "path": "/get_channels",
            }
        },
        {
            "name": "get_event",
            "table_name": "get_event",
            "endpoint": {
                "path": "/get_events",
                "params": {
                    # the parameters below can optionally be configured
                    # "is_filtered": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_notification",
            "table_name": "get_notification",
            "endpoint": {
                "path": "/get_notifications",
                "params": {
                    # the parameters below can optionally be configured
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_setting",
            "table_name": "get_setting",
            "endpoint": {
                "path": "/get_settings",
            }
        },
        {
            "name": "get_suggested_follows_all",
            "table_name": "get_suggested_follows_all",
            "endpoint": {
                "path": "/get_suggested_follows_all",
                "params": {
                    # the parameters below can optionally be configured
                    # "in_onboarding": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_users_for_topic",
            "table_name": "get_users_for_topic",
            "endpoint": {
                "path": "/get_users_for_topic",
                "params": {
                    # the parameters below can optionally be configured
                    # "topic_id": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_welcome_channel",
            "table_name": "get_welcome_channel",
            "endpoint": {
                "path": "/get_welcome_channel",
            }
        },
        ]
    }

    return rest_api_source(source_config)

2. Configuring your source and destination credentials​

info

dlt-init-openapi will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml.

  • If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
  • OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​


[runtime]
log_level="INFO"

[sources.clubhouse]
# Base URL for the API
base_url = "https://www.clubhouseapi.com/api/"

generated secrets.toml​


[sources.clubhouse]
# secrets for your clubhouse source
# example_api_key = "example value"

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations

At this time, the dlt-init-openapi cli tool will always create pipelines that load to a local duckdb instance. Switching to a different destination is trivial, all you need to do is change the destination parameter in clubhouse_pipeline.py to bigquery and supply the credentials as outlined in the destination doc linked below.

  • Read more about setting up the rest_api source in our docs.
  • Read more about setting up the BigQuery destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at clubhouse_pipeline.py, as well as a folder clubhouse that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


import dlt

from clubhouse import clubhouse_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="clubhouse_pipeline",
        destination='duckdb',
        dataset_name="clubhouse_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = clubhouse_source()
    info = pipeline.run(source)
    print(info)

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python clubhouse_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline clubhouse_pipeline info

You can also use streamlit to inspect the contents of your BigQuery destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline clubhouse_pipeline show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with GitHub Actions: Learn how to use GitHub Actions to deploy your dlt pipeline with a simple cron schedule. Github Actions
  • Deploy with Airflow: Deploy your dlt pipeline using Airflow and Google Composer for a managed workflow orchestration. Airflow
  • Deploy with Google Cloud Functions: Use Google Cloud Functions to deploy your dlt pipeline in a serverless environment. Google cloud functions
  • Explore other deployment options: Discover various other methods to deploy your dlt pipeline. and others...

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth operation and quick identification of issues. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified about any critical issues or changes in your dlt pipeline, ensuring you can respond promptly to any problems. Set up alerts
  • And set up tracing: Implement tracing to gain detailed insights into the performance and execution of your dlt pipeline, helping you to troubleshoot and optimize your data workflows. And set up tracing

Available Sources and Resources​

For this verified source the following sources and resources are available

Source Clubhouse​

Clubhouse source provides notifications, user suggestions, events, topics, settings, updates, and channels data.

Resource NameWrite DispositionDescription
get_notificationappendRetrieves notifications for the user.
get_suggested_follows_allappendFetches a list of all suggested users to follow.
get_eventappendRetrieves details about a specific event.
get_users_for_topicappendGets a list of users associated with a specific topic.
get_all_topicappendRetrieves all available topics on the platform.
get_settingappendFetches user settings and preferences.
get_actionable_notificationappendRetrieves actionable notifications that require user interaction.
check_for_updateappendChecks if there are any updates available for the app.
get_channelappendRetrieves information about a specific audio channel.
get_welcome_channelappendFetches details about the welcome channel for new users.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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