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Python Data Loading from hubspot to snowflake using dlt Library

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This guide provides instructions on how to load data from HubSpot, a customer relationship management (CRM) software and inbound marketing platform, to Snowflake, a cloud-based data warehousing platform. The process involves using an open-source Python library called dlt. HubSpot helps businesses attract visitors, engage customers, and close leads. Snowflake, on the other hand, enables the storage, processing, and analysis of large volumes of data. The dlt library simplifies the data transfer from HubSpot to Snowflake. For more information about HubSpot, visit https://www.hubspot.com.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance with schema inference, evolution and alerts. The short, declarative code makes maintenance simple and straightforward. Learn more here.
  • Scalability and Flexibility: dlt can be run wherever Python runs - on Airflow, serverless functions, notebooks. It does not require any external APIs, backends, or containers and can scale on micro and large infrastructures alike. Find out more here.
  • User-friendly, Declarative Interface: dlt provides a user-friendly, declarative interface that makes it easy for beginners to use while still offering powerful features for senior professionals. Check out the Getting started guide.
  • Robust Governance Support: dlt pipelines offer robust governance support through key mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.
  • Community Support: dlt has a thriving community where users can ask questions, share how they use the library, and report problems or make feature requests. 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 Snowflake:

pip install "dlt[snowflake]"

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

# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot snowflake
# 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[snowflake]>=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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # 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 Snowflake 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='snowflake',
    )

    # 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='snowflake',
    )

    # 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='snowflake',
    )

    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='snowflake',
        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 Snowflake 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 can be deployed using Github Actions which is a CI/CD runner. You can specify when the GitHub Action should run using a cron schedule expression. Find more details on how to do it here.
  • Deploy with Airflow: Airflow is another platform where you can deploy your dlt pipeline. Google provides a managed Airflow environment known as Google Composer. Learn more about deploying with Airflow here.
  • Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions which is a serverless execution environment for building and connecting cloud services. Find the detailed guide here.
  • Other Deployment Options: There are other options available for deploying dlt. You can find more details here.

The running in production section will teach you about:​

  • Monitoring your pipeline: dlt provides comprehensive monitoring capabilities for your data pipelines. You can easily track the progress of your data loads, check the status of individual jobs, and get detailed information about any errors that occur. Find out more here.
  • Setting up alerts: Stay informed about the status of your pipelines with dlt's alerting feature. You can set up alerts to notify you of any changes or issues with your data loads, ensuring you can respond quickly to any problems. Learn how to set up alerts here.
  • Setting up tracing: dlt's tracing feature allows you to track the flow of data through your pipeline. This can be invaluable for debugging issues, optimizing performance, and gaining insights into your data processing. Discover how to set up tracing here.

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!

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