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Python Data Loading from airtable to redshift using dlt Library

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This page provides technical documentation on how to load data from airtable to redshift using the open-source Python library, dlt. airtable is a cloud-based platform that combines spreadsheet and database functionalities, simplifying data management and collaboration. On the other hand, redshift is a fully managed, petabyte-scale data warehouse service in the cloud, capable of scaling from a few hundred gigabytes to a petabyte or more. For additional information about airtable, visit https://www.airtable.com/. The documentation will guide you through the process of using dlt to facilitate data transfer between these two platforms.

dlt Key Features​

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go. For more information, visit the Getting Started Guide.

  • Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. More details can be found here.

  • Wide Range of Data Types: dlt supports a wide range of data types, including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. Learn more about the data types here.

  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, including running extraction, normalization and load in parallel, writing sources and resources that run in parallel via thread pools and async execution, and finetuning the memory buffers, intermediary file sizes and compression options. Read more about performance here.

  • Community Support: dlt has a strong community that is always ready to help. You can give the library a star on GitHub, ask questions and share how you use the library on Slack, and report problems and make feature requests 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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir airtable_pipeline
cd airtable_pipeline
# initialize a new pipeline with your source and destination
dlt init airtable redshift
# 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[redshift]>=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.redshift.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 = 5439
connect_timeout = 15

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 Redshift 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='redshift', 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='redshift', 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='redshift', 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='redshift', 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 Redshift 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 pipeline using Github Actions. This feature allows for continuous integration and delivery, making your pipeline easily maintainable and scalable.
  • Deploy with Airflow: With dlt, you can deploy your pipeline with Airflow, a platform designed to programmatically author, schedule, and monitor workflows.
  • Deploy with Google Cloud Functions: dlt also supports deployment using Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: dlt offers a variety of other deployment options to fit your specific needs. You can explore these options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to monitor your pipeline in real-time, giving you insights into its performance and any potential issues. You can learn more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues that might occur during the pipeline's execution. This ensures that you can quickly react to any problems. Check out this guide on how to set up alerts here.
  • Set Up Tracing: Tracing is a powerful feature of dlt that allows you to track the execution of your pipeline. This can be incredibly useful for debugging and performance optimization. Learn more about setting 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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