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Load Data from airtable to clickhouse Using Python's dlt Library

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This page provides technical documentation about using the open-source Python library dlt to load data from Airtable into ClickHouse. Airtable is a cloud-based platform that combines spreadsheet and database functionalities, facilitating efficient data management and collaboration. On the other hand, ClickHouse is a swift open-source column-oriented database management system, capable of generating real-time analytical data reports using SQL queries. By utilizing dlt, users can seamlessly transfer data from Airtable to ClickHouse, optimizing data analysis processes. For more information about Airtable, visit https://www.airtable.com/.

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. Get started with dlt
  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more about dlt
  • Supports various data types: dlt supports a variety of data types including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. Check out the supported data types
  • Step-by-step guidance: dlt provides a detailed tutorial, providing a step-by-step guide to building a pipeline that loads data from an API into DuckDB. Follow the tutorial
  • Supports DuckDB destination: dlt supports DuckDB as a destination. You can configure the file formats to load data to DuckDB and it supports all write dispositions. Learn more about DuckDB destination

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 ClickHouse:

pip install "dlt[clickhouse]"

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

# create a new directory
mkdir airtable_pipeline
cd airtable_pipeline
# initialize a new pipeline with your source and destination
dlt init airtable clickhouse
# 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[clickhouse]>=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.clickhouse]
dataset_name = "dataset_name" # please set me up!

[destination.clickhouse.credentials]
database = "default"
password = "password" # please set me up!
username = "default"
host = "host" # please set me up!
port = 9440
http_port = 8443

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 ClickHouse 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='clickhouse', 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='clickhouse', 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='clickhouse', 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='clickhouse', 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 ClickHouse 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 provides a straightforward method to deploy your pipelines using Github Actions. You can specify when the GitHub Action should run using a cron schedule expression. Learn more about the deployment process here.
  • Deploy with Airflow: dlt can be deployed using Airflow, a platform to programmatically author, schedule and monitor workflows. Google Composer, a managed Airflow environment provided by Google, is also supported. Check out the detailed guide here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. dlt supports deployment on Google Cloud Functions. To understand the deployment process, follow this guide.
  • Other Deployment Methods: dlt supports various other deployment methods. You can choose the one that best suits your needs. Explore other deployment methods here.

The running in production section will teach you about:​

  • Monitor your Pipeline: Learn how to keep track of your pipeline's performance and troubleshoot any issues that may arise during its execution. Check out the guide on How to Monitor your pipeline.
  • Set up Alerts: Stay informed about the status of your pipeline by setting up alerts. This will help you respond quickly to any issues that may affect your pipeline's performance. Learn more about it in the Set up alerts guide.
  • Implement Tracing: Tracing allows you to track the flow of data through your pipeline. This can be useful for debugging and performance optimization. Find out how to set it up in the Set up 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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