Python Data Loading from hubspot
to postgresql
using dlt
Library
Join our Slack community or book a call with our support engineer Violetta.
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 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.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
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 Aladtec to Redshift in python with dlt
- Load data from Apple App-Store Connect to Azure Synapse in python with dlt
- Load data from Sentry to Supabase in python with dlt
- Load data from Chargebee to Neon Serverless Postgres in python with dlt
- Load data from Crypt API to Timescale in python with dlt
- Load data from Google Sheets to EDB BigAnimal in python with dlt
- Load data from Looker to BigQuery in python with dlt
- Load data from Zendesk to Neon Serverless Postgres in python with dlt
- Load data from Mux to Supabase in python with dlt
- Load data from MySQL to AlloyDB in python with dlt