Loading Data from IFTTT
to AWS S3
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. The filesystem AWS S3
destination stores data on AWS S3
, allowing the creation of datalakes. Data can be uploaded as JSONL, Parquet, or CSV. Using the open-source Python library dlt
, you can efficiently load data from IFTTT
to AWS S3
. Further information about IFTTT
can be found at IFTTT.
dlt
Key Features
- Fetching data from the GitHub API: Learn how to efficiently fetch data from the GitHub API using
dlt
. Read more - Understanding and managing data loading behaviors: Understand how to manage data loading behaviors, including appending and replacing data. Read more
- Incrementally loading new data and deduplicating existing data: Learn how to incrementally load new data and deduplicate existing data to keep your dataset up-to-date. Read more
- Making our data fetch more dynamic and reducing code redundancy: Discover techniques to make your data fetch more dynamic and reduce code redundancy. Read more
- Securely handling secrets: Understand best practices for securely handling secrets in your data 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 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.
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 AWS S3
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 a pipeline using GitHub Actions, a CI/CD runner that you can use for free. Read more
- Deploy with Airflow and Google Composer: Discover how to deploy a pipeline with Airflow and Google Composer, a managed Airflow environment provided by Google. Read more
- Deploy with Google Cloud Functions: Find out how to deploy a pipeline using Google Cloud Functions for a serverless deployment. Read more
- Explore More Deployment Options: Check out other methods to deploy your pipeline using different platforms and services. Read more
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 issue resolution. How to Monitor your pipeline - Set up alerts: Set up alerts to stay informed about the status of your
dlt
pipeline and be notified of any issues promptly. Set up alerts - Set up tracing: Implement tracing to gain insights into the pipeline's performance and debug issues efficiently. 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 |
Additional pipeline guides
- Load data from GitHub to Azure Cosmos DB in python with dlt
- Load data from Aladtec to Snowflake in python with dlt
- Load data from Notion to Google Cloud Storage in python with dlt
- Load data from MySQL to EDB BigAnimal in python with dlt
- Load data from Braze to Azure Synapse in python with dlt
- Load data from Google Cloud Storage to DuckDB in python with dlt
- Load data from IFTTT to PostgreSQL in python with dlt
- Load data from GitLab to Microsoft SQL Server in python with dlt
- Load data from MySQL to Azure Synapse in python with dlt
- Load data from PostgreSQL to Supabase in python with dlt