Loading Data from Clubhouse
to Azure Cloud Storage
with dlt
in Python
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Loading data from Clubhouse
to Azure Cloud Storage
can be efficiently managed using the open-source Python library dlt
. Clubhouse
is a social media audio app that provides real-time virtual rooms for users to communicate via audio. Azure Cloud Storage
is a destination that stores data on Microsoft Azure, allowing for the creation of data lakes. With dlt
, you can easily upload data in formats such as JSONL, Parquet, or CSV. This documentation will guide you through the process of extracting data from Clubhouse
and loading it into Azure Cloud Storage
using dlt
. For more details about Clubhouse
, visit clubhouse.com.
dlt
Key Features
- Fetching data from the GitHub API: Learn how to fetch data from the GitHub API using
dlt
. Read more - Understanding and managing data loading behaviors: Understand how to manage data loading behaviors effectively. Read more
- Incrementally loading new data and deduplicating existing data: Learn how to load only new data and deduplicate existing data. Read more
- Making our data fetch more dynamic and reducing code redundancy: Discover how to make your data fetch more dynamic and reduce code redundancy. Read more
- Securely handling secrets: Learn how to handle secrets securely in your
dlt
pipeline. Read more
Getting started with your pipeline locally
dlt-init-openapi
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
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
.
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
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 filesystem and supply the credentials as outlined in the destination doc linked below.
The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials]
section in your secrets.toml
.
[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"
By default, the filesystem destination will store your files as JSONL
. You can tell your pipeline to choose a different format with the loader_file_format
property that you can set directly on the pipeline or via your config.toml
. Available values are jsonl
, parquet
and csv
:
[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"
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 Azure Cloud Storage
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 deploy your
dlt
pipeline using GitHub Actions. - Deploy with Airflow and Google Composer: Follow the steps to deploy your
dlt
pipeline with Airflow and Google Composer. - Deploy with Google Cloud Functions: Discover how to deploy your
dlt
pipeline using Google Cloud Functions. - Explore Other Deployment Options: Check out additional methods for deploying your
dlt
pipeline here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline in production by following this guide. - Set up alerts: Ensure you are immediately informed of any issues by setting up alerts as described here.
- And set up tracing: Understand the detailed execution of your
dlt
pipeline by setting up tracing with the help of this documentation.
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
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