Python Data Loading from hubspot to postgresql 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 Adrian.

dlt is an open-source Python library that facilitates the transfer of data from various sources to different destinations. This document provides a guide on how to use dlt to load data from HubSpot to PostgreSQL. HubSpot is a Customer Relationship Management (CRM) software and inbound marketing platform that assists businesses in attracting visitors, engaging customers, and closing leads. On the other hand, PostgreSQL is a powerful, open-source object-relational database system that employs and extends the SQL language, equipped with numerous features that securely store and scale the most complex data workloads. More details about HubSpot can be found at https://www.hubspot.com.

dlt Key Features

• Automated Maintenance: With features like schema inference and evolution, as well as alerts, dlt makes maintenance a breeze. With its short, declarative code, you can easily manage and maintain your data pipelines. Read more
• Run Anywhere: dlt is designed to run wherever Python runs. Be it on Airflow, serverless functions, or notebooks, dlt can be deployed without the need for external APIs, backends, or containers. It scales on both micro and large infrastructures. Read more
• User-friendly Interface: dlt offers a declarative interface that is easy to use for beginners, while still offering powerful tools for senior professionals. 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. These features contribute to better data management practices, compliance adherence, and overall data governance. Read more
• Integration with Multiple Sources: dlt can easily integrate with various data sources like Postgres and load data to the destination of your choice. This makes it a versatile tool for managing your data pipelines.

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 PostgreSQL:

pip install "dlt[postgres]"

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

# create a new directory
mkdir my-hubspot-pipeline
cd my-hubspot-pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot postgres
# 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[postgres]>=0.3.25

You now have the following folder structure in your project:

my-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:

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

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.postgres.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!
port = 5432
connect_timeout = 15

Further help setting up your source and destinations

Please consult the detailed setup instructions for the PostgreSQL destination in the dlt destinations documentation.

Likewise you can find the setup instructions for HubSpot source in the dlt verifed sources documentation.

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='postgres',
    )

    # 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='postgres',
    )

    # 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='postgres',
    )

    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='postgres',
        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 PostgreSQL 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. This method uses a CI/CD runner that is essentially free to use.
• Deploy with Airflow: You can also use Airflow to deploy your dlt project. This method involves the creation of an Airflow DAG for your pipeline script.
• Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This serverless execution environment runs your code on demand.
• Other Deployment Methods: There are other ways to deploy your dlt project. Check out the deployment documentation for more options and information.

The running in production section will teach you about:

• Monitor your pipeline: With dlt, you can easily monitor your pipeline to ensure it's running smoothly. Check out the how to monitor your pipeline guide for more information.
• Set up alerts: Stay on top of any issues with your pipeline by setting up alerts. dlt makes it simple to set up alerts so you can be notified of any problems. Learn more from the set up alerts guide.
• Set up tracing: Tracing allows you to track the execution of your pipeline and identify any potential issues. dlt provides easy-to-use tools for setting up tracing. Check out the set up tracing guide for more information.

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

Additional pipeline guides

• Load data from Notion to Databricks in python with dlt
• Load data from Slack to AWS Athena in python with dlt
• Load data from Salesforce to AWS S3 in python with dlt
• Load data from Google Analytics to Databricks in python with dlt
• Load data from Airtable to PostgreSQL in python with dlt
• Load data from Notion to Azure Synapse in python with dlt
• Load data from Shopify to Snowflake in python with dlt
• Load data from Mux to Databricks in python with dlt
• Load data from Mux to BigQuery in python with dlt
• Load data from Shopify to Snowflake in python with dlt