Load Data from IFTTT
to Neon Serverless Postgres
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to Neon Serverless Postgres. You can get the connection string for your Neon Serverless Postgres database as described in the Neon Serverless Postgres Docs.
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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. This documentation will guide you on how to load data from IFTTT
to Neon Serverless Postgres
using the open-source python library dlt
. Neon Serverless Postgres
is a serverless platform designed to help you build reliable and scalable applications faster. This guide will help you leverage dlt
to efficiently transfer data, enhancing your productivity and connectivity across different platforms and smart devices. For more information on IFTTT
, visit ifttt.com.
dlt
Key Features
- Automated maintenance: With schema inference, evolution, and alerts,
dlt
simplifies maintenance. Learn more - Run it where Python runs: Compatible with Airflow, serverless functions, and notebooks. Scales on micro and large infrastructures alike. Learn more
- User-friendly interface: The declarative interface removes knowledge obstacles for beginners while empowering senior professionals. Learn more
- Fetching data from APIs: Easily load data from various APIs into your chosen destination. Learn more
- Scalability: Utilize iterators, chunking, and parallelization for efficient processing of large datasets. Learn 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 postgres and supply the credentials as outlined in the destination doc linked below.
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 Neon Serverless Postgres
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 pipeline using GitHub Actions for CI/CD. Read more
- Deploy with Airflow and Google Composer: Follow this guide to deploy your pipeline using Airflow and Google Composer. Read more
- Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions. Read more
- Explore other deployment options: Check out various other methods to deploy your pipeline. 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 it runs smoothly by following the guide here. - Set up alerts: Set up alerts to be notified of any issues or important events in your
dlt
pipeline by following the instructions here. - Set up tracing: Implement tracing to gain detailed insights and track the performance of your
dlt
pipeline by following the steps here.
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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