Skip to main content

Python Data Loading from hubspot to motherduck using dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This page provides technical documentation on how to load data from HubSpot, a customer relationship management (CRM) software and inbound marketing platform, to MotherDuck, a fast in-process analytical database. The process is facilitated through the use of dlt, an open-source Python library. HubSpot aids businesses in attracting visitors, engaging customers, and closing leads, while MotherDuck supports a feature-rich SQL dialect with deep integrations into client APIs. dlt serves as the bridge, enabling efficient data transfer from HubSpot to MotherDuck. More information about HubSpot can be found at https://www.hubspot.com.

dlt Key Features​

  • HubSpot Verified Source: dlt provides a verified source for HubSpot, allowing you to easily load data from HubSpot API to your desired destination. Learn more
  • MotherDuck Destination: dlt supports MotherDuck as a destination, providing an efficient way to move data to a remote DuckDB database. It also offers full support for dlt state sync. Learn more
  • Comprehensive Getting Started Guide: dlt offers a detailed tutorial to help you build a pipeline that loads data from the GitHub API into DuckDB. The guide covers everything from creating a pipeline to exploring the loaded data. Learn more
  • Salesforce Verified Source: dlt provides a verified source for Salesforce, enabling you to load data from Salesforce API to the destination of your choice. Learn more
  • Shopify Verified Source: dlt offers a verified source for Shopify, allowing you to load data from Shopify API or Shopify Partner API to your desired destination. 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 MotherDuck:

pip install "dlt[motherduck]"

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

# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot motherduck
# 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:

dlt[motherduck]>=0.3.25

You now have the following folder structure in your project:

hubspot_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── hubspot/                   # folder with source specific files
│   └── ...
├── hubspot_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

generated secrets.toml​

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

[sources.hubspot]
api_key = "api_key" # please set me up!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # 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 HubSpot source in our docs.
  • Read more about setting up the MotherDuck 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 hubspot_pipeline.py, as well as a folder hubspot 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
import dlt

from hubspot import hubspot, hubspot_events_for_objects, THubspotObjectType


def load_crm_data() -> None:
    """
    This function loads all resources from HubSpot CRM

    Returns:
        None
    """

    # Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
    # Add full_refresh=(True or False) if you need your pipeline to create the dataset in your destination
    p = dlt.pipeline(
        pipeline_name="hubspot",
        dataset_name="hubspot_dataset",
        destination='motherduck',
    )

    # Run the pipeline with the HubSpot source connector
    info = p.run(hubspot())

    # Print information about the pipeline run
    print(info)


def load_crm_data_with_history() -> None:
    """
    Loads all HubSpot CRM resources and property change history for each entity.
    The history entries are loaded to a tables per resource `{resource_name}_property_history`, e.g. `contacts_property_history`

    Returns:
        None
    """

    # Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
    # Add full_refresh=(True or False) if you need your pipeline to create the dataset in your destination
    p = dlt.pipeline(
        pipeline_name="hubspot",
        dataset_name="hubspot_dataset",
        destination='motherduck',
    )

    # Configure the source with `include_history` to enable property history load, history is disabled by default
    data = hubspot(include_history=True)

    # Run the pipeline with the HubSpot source connector
    info = p.run(data)

    # Print information about the pipeline run
    print(info)


def load_crm_objects_with_custom_properties() -> None:
    """
    Loads CRM objects, reading only properties defined by the user.
    """

    # Create a DLT pipeline object with the pipeline name,
    # dataset name, properties to read and destination database
    # type Add full_refresh=(True or False) if you need your
    # pipeline to create the dataset in your destination
    p = dlt.pipeline(
        pipeline_name="hubspot",
        dataset_name="hubspot_dataset",
        destination='motherduck',
    )

    source = hubspot()

    # By default, all the custom properties of a CRM object are extracted,
    # ignoring those driven by Hubspot (prefixed with `hs_`).

    # To read fields in addition to the custom ones:
    # source.contacts.bind(props=["date_of_birth", "degree"])

    # To read only two particular fields:
    source.contacts.bind(props=["date_of_birth", "degree"], include_custom_props=False)

    # Run the pipeline with the HubSpot source connector
    info = p.run(source)

    # Print information about the pipeline run
    print(info)


def load_web_analytics_events(
    object_type: THubspotObjectType, object_ids: List[str]
) -> None:
    """
    This function loads web analytics events for a list objects in `object_ids` of type `object_type`

    Returns:
        None
    """

    # Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
    p = dlt.pipeline(
        pipeline_name="hubspot",
        dataset_name="hubspot_dataset",
        destination='motherduck',
        full_refresh=False,
    )

    # you can get many resources by calling this function for various object types
    resource = hubspot_events_for_objects(object_type, object_ids)
    # and load them together passing resources in the list
    info = p.run([resource])

    # Print information about the pipeline run
    print(info)


if __name__ == "__main__":
    # Call the functions to load HubSpot data into the database with and without company events enabled
    load_crm_data()
    load_crm_data_with_history()
    load_web_analytics_events("company", ["7086461639", "7086464459"])
    load_crm_objects_with_custom_properties()

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

python hubspot_pipeline.py

4. Inspecting your load result​

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

dlt pipeline hubspot info

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

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline hubspot 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 supports deployment using Github Actions. This allows you to automate your workflows and ensure your pipeline is always up-to-date.
  • Deploy with Airflow: dlt can also be deployed using Airflow. This provides a robust and scalable solution for managing complex workflows.
  • Deploy with Google Cloud Functions: For cloud-based deployments, dlt supports Google Cloud Functions. This serverless execution environment allows you to build and deploy applications on Google Cloud.
  • Other Deployment Options: dlt provides flexibility with numerous deployment options. You can explore more about these options here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt offers comprehensive monitoring capabilities for your data pipelines. It provides detailed insights into the pipeline's status, performance, and any issues that may arise. Learn more on how to effectively monitor your pipeline.
  • Set up alerts: Stay ahead of any potential issues with dlt's alerting feature. You can set up alerts to notify you of any significant events or changes in your pipeline's performance. Find out how to set up alerts.
  • Enable tracing: dlt allows you to trace your pipeline's execution, providing a detailed view of each step's performance. This feature is instrumental in identifying bottlenecks and optimizing your pipeline. Learn how to set up tracing.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source hubspot​

Hubspot source provides data on companies, contacts, deals, and customer service tickets.

Resource NameWrite DispositionDescription
companiesreplaceInformation about organizations
contactsreplaceVisitors, potential customers, leads
dealsreplaceDeal records, deal tracking
productsreplacePricing information of a product
quotesreplacePrice proposals that salespeople can create and send to their contacts
ticketsreplaceRequest for help from customers or users

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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.