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Python Data Loading from airtable to mssql using dlt

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This page provides technical documentation on how to load data from Airtable to Microsoft SQL Server using the open source Python library dlt. Airtable is a cloud-based platform that combines spreadsheet and database functionalities, making data management and collaboration simple. On the other hand, Microsoft SQL Server is a relational database management system (RDBMS) that interacts with applications and tools through Transact-SQL. By using dlt, data can be efficiently transferred from Airtable to Microsoft SQL Server. More details about Airtable can be found at https://www.airtable.com/.

dlt Key Features​

  • Automated Maintenance: dlt takes care of schema inference and evolution and alerts, making maintenance simple and efficient. You can learn more about this feature here.
  • Runs Anywhere Python Runs: dlt can run on Airflow, serverless functions, notebooks, and more. No external APIs, backends, or containers are required. You can learn more about this feature here.
  • Supports Multiple Destinations: dlt supports multiple destinations including Microsoft SQL Server and DuckDB. You can learn more about these destinations here and here.
  • Data Loading: dlt loads data using the Airtable API to the destination of your choice. You can learn more about this feature here.
  • Community Support: dlt has a strong community where you can ask questions, share your usage of the library, and report problems or make feature requests. You can join the community here.

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 Microsoft SQL Server:

pip install "dlt[mssql]"

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 Microsoft SQL Server. You can run the following commands to create a starting point for loading data from Airtable to Microsoft SQL Server:

# create a new directory
mkdir airtable_pipeline
cd airtable_pipeline
# initialize a new pipeline with your source and destination
dlt init airtable mssql
# 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[mssql]>=0.3.25

You now have the following folder structure in your project:

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. The automatically created version of these files look like this:

generated 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!

generated 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.mssql.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 1433
connect_timeout = 15
driver = "driver" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Airtable source in our docs.
  • Read more about setting up the Microsoft SQL Server 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 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='mssql', 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='mssql', 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='mssql', 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='mssql', 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 Microsoft SQL Server 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 easily deploy your pipeline using Github Actions. You can schedule the run time using a cron schedule expression. Learn more about this deployment method here.
  • Deploy with Airflow: You can use dlt to deploy your pipeline with Airflow. This method is particularly useful if you're using Google's managed Airflow environment, Google Composer. Find out more about deploying with Airflow here.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services. To learn more about this deployment option, check out this guide.
  • Other Deployment Methods: dlt supports various other deployment methods. You can explore more about these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides detailed monitoring capabilities for your data pipelines. You can easily track the progress of your pipeline, identify potential issues, and diagnose problems using the built-in monitoring tools. More on this can be found on the Monitoring Guide.
  • Set Up Alerts: With dlt, you can set up alerts to notify you about any issues or significant events that occur during the execution of your data pipeline. This feature ensures that you are always aware of the status of your pipeline and can react promptly to any issues. Learn how to set up alerts in the Alerting Guide.
  • Set Up Tracing: dlt also allows you to set up tracing for your data pipelines. This feature provides you with detailed insights into the execution of your pipeline, helping you to identify bottlenecks and optimize your pipeline's performance. Find out how to set up tracing in the Tracing Guide.

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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