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Python Data Transfer from airtable to bigquery with dlt Library

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Airtable to BigQuery. Airtable is a cloud-based platform that combines the functionalities of spreadsheets and databases, making data management and collaboration straightforward. On the other hand, BigQuery is a serverless, cost-effective enterprise data warehouse that operates across multiple clouds and scales with your data. By leveraging the capabilities of dlt, you can effectively transfer data between these two platforms. More details about Airtable can be found at https://www.airtable.com/.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance with schema inference and evolution and alerts. With short declarative code, maintenance becomes simple. Learn more
  • Versatile: dlt can run wherever Python runs - on Airflow, serverless functions, notebooks. It does not require external APIs, backends, or containers, and scales on both micro and large infrastructures. Learn more
  • Data Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Learn more
  • Normalization and Loading: dlt automatically turns JSON returned by any source (e.g., an API) into a live dataset stored in the destination of your choice. It does this by first extracting the JSON data, then normalizing it to a schema, and finally loading it to the location where you will store it. Learn more
  • Community Support: dlt has a strong community of users who can provide help and share their experiences. Users can give the library a star on GitHub, ask questions and share how they use the library on Slack, and report problems and make feature requests. Learn more

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 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​

First you need to install the dlt library with the correct extras for BigQuery:

pip install "dlt[bigquery]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Airtable to BigQuery. You can run the following commands to create a starting point for loading data from Airtable to BigQuery:

# create a new directory
mkdir my-airtable-pipeline
cd my-airtable-pipeline
# initialize a new pipeline with your source and destination
dlt init airtable bigquery
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

pyairtable~=2.1
dlt[bigquery]>=0.3.25

You now have the following folder structure in your project:

my-airtable-pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── airtable/                   # folder with source specific files
│   └── ...
├── airtable_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

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:

config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

[sources.airtable]
base_id = "base_id" # please set me up!

secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.airtable]
access_token = "access_token" # please set me up!

[destination.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the BigQuery destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Airtable source in the dlt verifed sources documentation.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at airtable_pipeline.py, as well as a folder airtable 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:

from typing import List, Dict, Any

import dlt

from airtable import airtable_source


def load_entire_base(base_id: str, resources_to_apply_hints: Dict[str, Any]) -> None:
    """
    Loads all tables from the specified Airtable base.

    Args:
        base_id (str): The id of the base. Obtain it, e.g. from the URL in your web browser.
            It starts with "app". See https://support.airtable.com/docs/finding-airtable-ids
        resources_to_apply_hints (dict): Dict of table names and fields we want to apply hints.

    Note:
        - The base_id can either be passed directly or set up in ".dlt/config.toml".
    """
    # configure the pipeline with your destination details
    pipeline = dlt.pipeline(
        pipeline_name="airtable", destination='bigquery', dataset_name="airtable_data"
    )

    # Retrieve data from Airtable using airtable_source.
    airtables = airtable_source(base_id=base_id)

    # typing columns to silence warnings
    for resource_name, field_names in resources_to_apply_hints.items():
        for field_name in field_names:
            airtables.resources[resource_name].apply_hints(
                columns={field_name: {"name": field_name, "data_type": "text"}}
            )

    load_info = pipeline.run(airtables, write_disposition="replace")
    print(load_info)


def load_select_tables_from_base_by_id(base_id: str, table_names: List[str]) -> None:
    """
    Load specific table IDs from Airtable to a data pipeline.

    Args:
        base_id (str): The id of the base. Obtain it, e.g. from the URL in your web browser.
            It starts with "app". See https://support.airtable.com/docs/finding-airtable-ids
        table_names (List[str]): A list of table IDs or table names to load. Unless specified otherwise,
            all tables in the schema are loaded.  Names are freely user-defined. IDs start with "tbl".
            See https://support.airtable.com/docs/finding-airtable-ids
        resources_to_apply_hints (dict): Dict of table names and fields we want to apply hints.

    Note:
        - Filtering by names is less reliable than filtering on IDs because names can be changed by Airtable users.
        - Example in this Airtable URL: https://airtable.com/app7RlqvdoOmJm9XR/tblKHM5s3AujfSbAH
        - Table ID: "tblKHM5s3AujfSbAH"
        - The base_id and table_names can either be passed directly or set up in ".dlt/config.toml".
    """

    # configure the pipeline with your destination details
    pipeline = dlt.pipeline(
        pipeline_name="airtable", destination='bigquery', dataset_name="airtable_data"
    )

    airtables = airtable_source(
        base_id=base_id,
        table_names=table_names,
    )

    load_info = pipeline.run(airtables, write_disposition="replace")
    print(load_info)


def load_select_tables_from_base_by_name(
    base_id: str, table_names: List[str], resources_to_apply_hints: Dict[str, Any]
) -> None:
    """
    Loads specific table names from an Airtable base.

    Args:
        base_id (str): The id of the base. Obtain it, e.g. from the URL in your web browser.
            It starts with "app". See https://support.airtable.com/docs/finding-airtable-ids
        table_names (List[str]): A list of table IDs or table names to load. Unless specified otherwise,
            all tables in the schema are loaded.  Names are freely user-defined. IDs start with "tbl".
            See https://support.airtable.com/docs/finding-airtable-idss
        resources_to_apply_hints (dict): Dict of table names and fields we want to apply hints.

    Note:
        - Filtering by names is less reliable than filtering on IDs because names can be changed by Airtable users.
        - Example in this Airtable URL: https://airtable.com/app7RlqvdoOmJm9XR/tblKHM5s3AujfSbAH
        - Table ID: "tblKHM5s3AujfSbAH"
        - The base_id and table_names can either be passed directly or set up in ".dlt/config.toml".
    """
    pipeline = dlt.pipeline(
        pipeline_name="airtable", destination='bigquery', dataset_name="airtable_data"
    )

    airtables = airtable_source(
        base_id=base_id,
        table_names=table_names,
    )

    # typing columns to silence warnings
    for resource_name, field_names in resources_to_apply_hints.items():
        for field_name in field_names:
            airtables.resources[resource_name].apply_hints(
                columns={field_name: {"name": field_name, "data_type": "text"}}
            )

    load_info = pipeline.run(airtables, write_disposition="replace")
    print(load_info)


def load_and_customize_write_disposition(
    base_id: str, table_names: List[str], resources_to_apply_hints: Dict[str, Any]
) -> None:
    """
    Loads data from a specific Airtable base's table with customized write disposition("merge") using field_name.

    Args:
        base_id (str): The id of the base. Obtain it, e.g. from the URL in your web browser.
            It starts with "app". See https://support.airtable.com/docs/finding-airtable-ids
        table_names (List[str]): A list of table IDs or table names to load. Unless specified otherwise,
            all tables in the schema are loaded.  Names are freely user-defined. IDs start with "tbl".
            See https://support.airtable.com/docs/finding-airtable-ids
        resources_to_apply_hints (dict): Dict of table names and fields we want to apply hints.


    Note:
        - Filtering by names is less reliable than filtering on IDs because names can be changed by Airtable users.
        - Example in this Airtable URL: https://airtable.com/app7RlqvdoOmJm9XR/tblKHM5s3AujfSbAH
        - Table ID: "tblKHM5s3AujfSbAH"
        - The base_id and table_names can either be passed directly or set up in ".dlt/config.toml".

    """
    pipeline = dlt.pipeline(
        pipeline_name="airtable", destination='bigquery', dataset_name="airtable_data"
    )

    airtables = airtable_source(
        base_id=base_id,
        table_names=table_names,
    )

    # typing columns to silence warnings
    for resource_name, field_names in resources_to_apply_hints.items():
        for field_name in field_names:
            airtables.resources[resource_name].apply_hints(
                primary_key=field_name,
                columns={field_name: {"name": field_name, "data_type": "text"}},
                write_disposition="merge",
            )

    load_info = pipeline.run(airtables)
    print(load_info)


if __name__ == "__main__":
    load_entire_base(
        base_id="app7RlqvdoOmJm9XR",
        resources_to_apply_hints={
            "🎤 Speakers": ["Name"],
            "📆 Schedule": ["Activity"],
            "🪑 Attendees": ["Name"],
            "💰 Budget": ["Item"],
        },
    )
    load_select_tables_from_base_by_id(
        base_id="app7RlqvdoOmJm9XR",
        table_names=["tblKHM5s3AujfSbAH", "tbloBrS8PnoO63aMP"],
    )
    load_select_tables_from_base_by_name(
        "app7RlqvdoOmJm9XR",
        table_names=["💰 Budget"],
        resources_to_apply_hints={"💰 Budget": ["Item"]},
    )
    load_and_customize_write_disposition(
        base_id="appcChDyP0pZeC76v",
        table_names=["tbl1sN4CpPv8pBll4"],
        resources_to_apply_hints={"Sheet1": ["Name"]},
    )

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python airtable_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline airtable info

You can also use streamlit to inspect the contents of your BigQuery destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline airtable 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: dlt allows you to deploy your pipelines using Github Actions. You can specify when the GitHub Action should run using a cron schedule expression. More details can be found here.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow. dlt provides an Airflow wrapper that makes this process simple and straightforward. Learn more about this here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is another option for deploying your dlt pipelines. You can find more details on how to deploy a pipeline with Google Cloud Functions here.
  • Other Deployment Options: Apart from the above-mentioned methods, dlt supports various other deployment options. You can find more information about these here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides you with the ability to monitor your pipeline's performance and status. You can inspect and save load info, runtime trace, and schema changes. This helps you to keep track of your pipeline's health and performance. Learn more about it here.
  • Set up alerts: With dlt, you can set up alerts for your pipeline. This will notify you of any issues or changes in your pipeline, allowing you to take immediate action if necessary. Find out how to set up alerts here.
  • Set up tracing: Tracing in dlt allows you to track the execution of your pipeline. It provides timing information on extract, normalize, and load steps and also all the config and secret values with full information from where they were obtained. Learn how to set up tracing here.

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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