Python Data Loading from HubSpot to ClickHouse with 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 ClickHouse, an open-source column-oriented database management system. The process utilizes an open-source Python library called dlt. HubSpot helps businesses attract visitors, engage customers, and close leads, while ClickHouse enables real-time analytical data reports using SQL queries. With dlt, you can efficiently transfer data between these two platforms. For more information about HubSpot, visit https://www.hubspot.com. This guide will walk you through the steps of using dlt to load HubSpot data into ClickHouse.

dlt Key Features

• Automated Maintenance: dlt provides automated maintenance through schema inference, evolution and alerts, and with short declarative code, maintenance becomes simple. Read More
• Scalability and Performance: dlt offers scalability through iterators, chunking, and parallelization techniques. It also provides configuration options to finetune memory buffers, intermediary file sizes and compression options for better performance. Read More
• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read More
• Versatility: dlt can run anywhere Python runs - on Airflow, serverless functions, notebooks, etc. It does not require external APIs, backends or containers, making it versatile for both micro and large infrastructures. Read More
• Community Support: dlt has a vibrant community on Slack where you can ask questions, share your use cases, and learn from others. You can also contribute to the project on GitHub. Join the Community

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

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

dlt[clickhouse]>=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.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 HubSpot 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 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='clickhouse',
    )

    # 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='clickhouse',
    )

    # 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='clickhouse',
    )

    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='clickhouse',
        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 ClickHouse 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 allows you to deploy your pipeline using Github Actions. This is a CI/CD runner that you can use for free.
• Deploy with Airflow: You can deploy your pipeline with Airflow. Google Composer is a managed Airflow environment provided by Google that you can use.
• Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This allows you to run your code without having to manage a server.
• Other Deployment Options: There are many other ways to deploy your dlt pipeline. You can find more information on the deployment documentation page.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt allows you to monitor your pipeline efficiently. You can inspect and save load info and trace, as well as inspect, save, and alert on schema changes. Learn more about how to monitor your pipeline here.
• Set Up Alerts: With dlt, you can set up alerts to stay informed about any potential issues with your pipeline. This feature helps to ensure that your pipeline runs smoothly and any issues are addressed promptly. Learn more about setting up alerts here.
• Set Up Tracing: Tracing is an essential feature of dlt that allows you to track the execution of your pipeline. It provides detailed information about the extract, normalize, and load steps of your pipeline. Learn more about setting 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 Name	Write Disposition	Description
companies	replace	Information about organizations
contacts	replace	Visitors, potential customers, leads
deals	replace	Deal records, deal tracking
products	replace	Pricing information of a product
quotes	replace	Price proposals that salespeople can create and send to their contacts
tickets	replace	Request for help from customers or users

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