Loading Data from Clubhouse to AWS Athena with dlt in Python

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

Clubhouse is a social media audio app that provides real-time virtual rooms where users can communicate via audio. AWS Athena is an interactive query service that allows you to analyze data in Amazon S3 using standard SQL. This documentation explains how to load data from Clubhouse to AWS Athena using the open-source python library, dlt. Our implementation also supports iceberg tables for efficient data management. For more information about Clubhouse, visit clubhouse.com.

dlt Key Features

• Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more.
• Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more.
• Schema Evolution: dlt alerts users to schema changes, allowing them to take necessary actions such as reviewing and validating the changes. Read more.
• Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, such as running extraction, normalization, and load in parallel. Read more.
• Advanced Topics: dlt is a constantly growing library that supports many features and use cases needed by the community. Read 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 athena 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 AWS Athena 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 AWS Athena 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: Automate your pipeline deployment using GitHub Actions with a simple cron schedule. Learn more here.
• Deploy with Airflow and Google Composer: Use a managed Airflow environment provided by Google to deploy your pipeline. Follow the guide here.
• Deploy with Google Cloud Functions: Leverage serverless functions on Google Cloud to deploy your pipeline. Detailed instructions can be found here.
• Explore Other Deployment Options: Discover various other methods to deploy your pipeline, including detailed walkthroughs and guides. Check them out here.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operations and timely detection of any issues. How to Monitor your pipeline
• Set up alerts: Set up alerts to get notified about important events and issues in your pipeline, helping you to respond quickly and maintain data integrity. Set up alerts
• And set up tracing: Implement tracing to get detailed insights into the execution of your pipeline, allowing for easier debugging and performance optimization. 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 Name	Write Disposition	Description
get_notification	append	Retrieves notifications for the user.
get_suggested_follows_all	append	Fetches a list of all suggested users to follow.
get_event	append	Retrieves details about a specific event.
get_users_for_topic	append	Gets a list of users associated with a specific topic.
get_all_topic	append	Retrieves all available topics on the platform.
get_setting	append	Fetches user settings and preferences.
get_actionable_notification	append	Retrieves actionable notifications that require user interaction.
check_for_update	append	Checks if there are any updates available for the app.
get_channel	append	Retrieves information about a specific audio channel.
get_welcome_channel	append	Fetches details about the welcome channel for new users.

Additional pipeline guides

• Load data from PostgreSQL to BigQuery in python with dlt
• Load data from Klaviyo to Redshift in python with dlt
• Load data from Spotify to Microsoft SQL Server in python with dlt
• Load data from PostgreSQL to The Local Filesystem in python with dlt
• Load data from Slack to AlloyDB in python with dlt
• Load data from Chargebee to AWS Athena in python with dlt
• Load data from Apple App-Store Connect to Azure Cosmos DB in python with dlt
• Load data from Sentry to Microsoft SQL Server in python with dlt
• Load data from Salesforce to Neon Serverless Postgres in python with dlt
• Load data from Capsule CRM to Timescale in python with dlt