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Python Data Loading from HubSpot to Azure Synapse 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 Azure Synapse, an analytics service that combines enterprise data warehousing and Big Data analytics. The process uses an open source Python library known as dlt. HubSpot aids businesses in attracting visitors, engaging customers, and closing leads. On the other hand, Azure Synapse provides limitless analytics capabilities. The dlt library serves as a bridge between these two platforms, facilitating the transfer of data. More information about HubSpot can be found at https://www.hubspot.com.

dlt Key Features

  • HubSpot Verified Source: dlt offers a verified source for HubSpot, a popular CRM software. It allows users to load data using the HubSpot API to the destination of their choice. More details
  • Azure Synapse Destination: Azure Synapse is supported as a destination in dlt. Users can install the DLT library with Synapse dependencies using pip install dlt[synapse]. More details
  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. More details
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. More details
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines. This includes running extraction, normalization, and load in parallel, writing sources and resources that run in parallel via thread pools and async execution, and fine-tuning the memory buffers, intermediary file sizes, and compression options. More details

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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Azure Synapse 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='synapse',
)

# 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='synapse',
)

# 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='synapse',
)

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='synapse',
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 Azure Synapse 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 provides an easy way to deploy your pipelines using Github Actions. You can schedule your pipelines to run at specific times and also run them manually or on push. Find out more on how to do it here.
  • Deploy with Airflow: If you are using Airflow for your data pipelines, dlt integrates seamlessly with it. It creates an Airflow DAG for your pipeline script that you can customize. Learn more about deploying with Airflow here.
  • Deploy with Google Cloud Functions: With dlt, you can also deploy your pipelines using Google Cloud Functions. This allows you to run your pipelines in a serverless environment. Find out how to deploy with Google Cloud Functions here.
  • Other Deployment Options: Apart from the above, dlt also provides other ways to deploy your pipelines like using AWS Lambda, Google Cloud Run, and more. You can find out more about these deployment options here.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides several ways to monitor your pipeline. You can view the status of your pipeline, inspect the load information, and even save the load info and trace for later analysis. For more details, check out the guide on How to Monitor your pipeline.
  • Set Up Alerts: With dlt, it's easy to set up alerts for your pipelines. You can get notified when there are schema changes, data validation errors, or when the pipeline fails or succeeds. Learn more about it in the Set up alerts guide.
  • Tracing Your Pipeline: dlt allows you to trace your pipeline, providing timing information for extract, normalize, and load steps. This can be particularly useful for debugging and performance optimization. Learn how to set up tracing in the Set up tracing guide.

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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