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Python Data Loading from hubspot CRM to google cloud storage via dlt

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This page provides technical documentation on how to load data from HubSpot, a customer relationship management (CRM) software and inbound marketing platform, to Google Cloud Storage, a filesystem destination for data storage on the Google Cloud Platform. This process is facilitated by the open-source Python library dlt. HubSpot aids businesses in attracting visitors, engaging customers, and closing leads. Google Cloud Storage offers the ability to create datalakes and upload data in JSONL, Parquet, or CSV formats. More information about HubSpot can be found at https://www.hubspot.com. The dlt library is instrumental in this data transfer process.

dlt Key Features​

  • Fetching data from the GitHub API: Learn how to efficiently fetch data from the GitHub API using dlt. Link
  • Understanding and managing data loading behaviors: Understand how to manage data loading behaviors, including how to append or replace your data. Link
  • Incrementally loading new data and deduplicating existing data: Get insights on how to load new data incrementally and how to deduplicate existing data. Link
  • Making our data fetch more dynamic and reducing code redundancy: Learn how to make your data fetch more dynamic and reduce code redundancy by grouping resources. Link
  • Securely handling secrets and making reusable data sources: Understand how to securely handle secrets and make reusable data sources. Link

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 Google Cloud Storage:

pip install "dlt[filesystem]"

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

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

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 Google Cloud Storage destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to Google Cloud Storage, update the [destination.filesystem.credentials] section in your secrets.toml.

[destination.filesystem.credentials]
client_email="Please set me up!"
private_key="Please set me up!"
project_id="Please set me up!"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem',
    )

    # 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='filesystem',
    )

    # 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='filesystem',
    )

    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='filesystem',
        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 Google Cloud Storage 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: Use GitHub Actions for a free CI/CD runner to automate your pipeline deployment. Learn more here.
  • Deploy with Airflow: Google Composer provides a managed Airflow environment for your pipeline. Get the details here.
  • Deploy with Google Cloud Functions: Leverage Google Cloud Functions for serverless deployment of your dlt pipeline. Find out how here.
  • Explore other deployment options: Discover various methods to deploy your dlt pipeline, including cloud providers and other tools. Explore more here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth and efficient data processing. For more details, visit How to Monitor your pipeline.
  • Set up alerts: Setting up alerts helps you stay informed about the status of your pipeline and quickly respond to any issues. Find out more at Set up alerts.
  • Set up tracing: Implement tracing to gain insights into the performance and behavior of your dlt pipeline. This helps in diagnosing issues and optimizing performance. Learn more at And 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!

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