Load Data from IFTTT
to Azure Cloud Storage
with dlt
in Python
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IFTTT
(If This Then That) is a web-based service that allows users to create automated actions (applets) between different applications and devices. By connecting various services and setting up triggers and actions, IFTTT
enables users to automate tasks and streamline workflows. It supports a wide range of integrations, making it a versatile tool for enhancing productivity and connectivity across different platforms and smart devices. This documentation covers how to load data from IFTTT
to Azure Cloud Storage
using the open-source python library dlt
. Azure Cloud Storage
stores data on Microsoft Azure, allowing you to easily create data lakes. You can upload data as JSONL, Parquet, or CSV formats. For more information on IFTTT
, visit ifttt.com.
dlt
Key Features
- Flexible Filesystem Support:
dlt
supports multiple filesystem destinations including AWS S3, Google Storage, and Azure Blob Storage. Learn more - Schema Enforcement and Curation: Ensure data consistency and quality by defining and enforcing schemas. Read more
- Incremental Data Loading: Load only new data and deduplicate existing data to optimize performance. Read more
- Securely Handling Secrets: Manage and store your credentials securely with
dlt
. Learn how - Scalable and Fine-tunable Pipelines: Run extraction, normalization, and load processes in parallel, and fine-tune memory buffers and file sizes. 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 ifttt_service --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/ifttt_service.yaml --global-limit 2
cd ifttt_service_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:
ifttt_service_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── ifttt_service/
│ └── __init__.py # TODO: possibly tweak this file
├── ifttt_service_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak ifttt_service/__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 ifttt_service source will look like this:
Click to view full file (53 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="ifttt_service_source", max_table_nesting=2)
def ifttt_service_source(
api_key: str = dlt.secrets.value,
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
"auth": {
"type": "api_key",
"api_key": api_key,
"name": "IFTTT-Service-Key",
"location": "header"
},
},
"resources":
[
{
"name": "statu",
"table_name": "statu",
"endpoint": {
"path": "/ifttt/v1/status",
"paginator": "auto",
}
},
{
"name": "user_info",
"table_name": "user_info",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "data",
"path": "/ifttt/v1/user/info",
"paginator": "auto",
}
},
]
}
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.ifttt_service]
# Base URL for the API
base_url = "{scheme}://{hostname}"
generated secrets.toml
[sources.ifttt_service]
# secrets for your ifttt_service source
api_key = "FILL ME OUT" # TODO: fill in your credentials
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 ifttt_service_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 ifttt_service_pipeline.py
, as well as a folder ifttt_service
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 ifttt_service import ifttt_service_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="ifttt_service_pipeline",
destination='duckdb',
dataset_name="ifttt_service_data",
progress="log",
export_schema_path="schemas/export"
)
source = ifttt_service_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 ifttt_service_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline ifttt_service_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 ifttt_service_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 this guide to deploy your
dlt
pipeline with Airflow and Google Composer. - Deploy with Google Cloud Functions: Explore how to deploy your
dlt
pipeline using Google Cloud Functions. - More Deployment Options: Check out other ways to deploy your
dlt
pipeline in the deployment walkthroughs.
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 everything runs smoothly. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified of any issues or important events in your
dlt
pipeline. Set up alerts - Set up tracing: Implement tracing to track the execution of your
dlt
pipeline and diagnose any problems. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source IFTTT
Fetches user data and status updates from IFTTT for automation and integration purposes.
Resource Name | Write Disposition | Description |
---|---|---|
user_info | append | Information about the user |
statu | append | Status updates or logs related to applet actions |
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