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Load Data from IFTTT to AWS Athena Using 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. AWS Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Our implementation also supports iceberg tables. This documentation explains how to load data from IFTTT to AWS Athena using the open-source Python library called dlt. For more information on IFTTT, visit ifttt.com.

dlt Key Features​

  • Fetching data from the GitHub API: Learn how to efficiently fetch data from the GitHub API using dlt. Read more
  • Managing data loading behaviors: Understand how to manage data loading behaviors such as append and replace. Read more
  • Incremental loading and deduplication: Discover how to incrementally load new data and deduplicate existing data. Read more
  • Dynamic data fetching and code reduction: Make your data fetch more dynamic and reduce code redundancy. Read more
  • Securely handling secrets: Learn best practices for securely handling secrets in your data pipeline. 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 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​

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.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​

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 ifttt_service_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 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 Athena 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.
  • Deploy with Airflow: Follow this guide to deploy your pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions.
  • Explore more deployment options: Check out additional methods for deploying your pipeline 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 operation and quick issue detection. Read more here.
  • Set up alerts: Set up alerts to get notified of any issues or significant events in your dlt pipeline. Detailed instructions can be found here.
  • Set up tracing: Implement tracing in your dlt pipeline to track data flow and performance metrics. Find out how 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 NameWrite DispositionDescription
user_infoappendInformation about the user
statuappendStatus updates or logs related to applet actions

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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