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Loading HubSpot Data to YugabyteDB with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to YugabyteDB. You can get the connection string for your YugabyteDB database as described in the YugabyteDB Docs.

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HubSpot is a customer relationship management (CRM) software and inbound marketing platform that helps businesses attract visitors, engage customers, and close leads. YugabyteDB is a distributed PostgreSQL database designed for modern applications. It offers built-in resilience, seamless scalability, and flexible geo-distribution using PostgreSQL-compatible and Cassandra-inspired APIs. This documentation provides technical guidance on loading data from HubSpot to YugabyteDB using the open-source Python library dlt. The dlt library simplifies the data extraction and loading process, ensuring efficient and reliable data transfer. For more information about HubSpot, visit hubspot.com.

dlt Key Features

  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
  • Run it where Python runs: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
  • User-friendly interface: A declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
  • Scalability via iterators, chunking, and parallelization: Efficient processing of large datasets by breaking them down into manageable chunks and leveraging parallel processing capabilities. Learn more
  • Proactive governance: Alerts users to schema changes, allowing them to take necessary actions to maintain data consistency and integrity. 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 YugabyteDB:

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

# create a new directory
mkdir hubspot_pipeline
cd 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:

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.postgres]
dataset_name = "dataset_name" # 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

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 YugabyteDB 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='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 YugabyteDB 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: Automate your pipeline deployment using GitHub Actions. This guide shows you how to set up continuous integration and deployment for your dlt pipelines.
  • Deploy with Airflow and Google Composer: Learn how to deploy your pipeline in a managed Airflow environment provided by Google with this Airflow deployment guide.
  • Deploy with Google Cloud Functions: Use serverless functions to run your dlt pipelines by following the steps in this Google Cloud Functions deployment guide.
  • Explore other deployment options: Discover more ways to deploy your dlt pipelines by checking out the comprehensive deployment guide.

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 operation and quick issue resolution. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified about any issues or important events in your dlt pipeline, ensuring you can take timely actions. Set up alerts
  • Set up tracing: Implement tracing to track the execution and performance of your dlt pipeline, helping you to diagnose and fix problems efficiently. 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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