Python Data Loading from hubspot
to aws s3
using dlt
Library
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dlt
is an open-source Python library that supports data loading from various sources to different destinations. One such source is HubSpot
, a customer relationship management (CRM) software and inbound marketing platform. HubSpot
aids businesses in attracting visitors, engaging customers, and closing leads. On the other hand, AWS S3
is a destination where dlt
can store data. AWS S3
is a filesystem that allows you to create data lakes by storing data in formats such as JSONL, Parquet, or CSV. For additional information on HubSpot
, visit https://www.hubspot.com. This technical documentation will guide you on how to load data from HubSpot
to AWS S3
using dlt
.
dlt
Key Features
HubSpot Integration: The
dlt
verified source for HubSpot allows you to load data using the HubSpot API to the destination of your choice. This includes data about contacts, companies, deals, tickets, products, quotes, and web analytics events for a given object type and object ids.Advanced Deployment Options:
dlt
provides advanced options for deploying from a branch of theverified-sources
repo or from another repo. You can learn more about these options in the Add a Verified Source section.Salesforce Integration: The
dlt
verified source for Salesforce allows you to load data using the Salesforce API to the destination of your choice. This includes data about users, user roles, leads, contacts, campaigns, products, price books, price book entries, and more.Filesystem & Buckets:
dlt
supports storing data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. You can learn more about this feature in the Filesystem section.Zendesk Integration: The
dlt
verified source for Zendesk allows you to load data using the Zendesk Support API, Zendesk Chat API, and Zendesk Talk API to the destination of your choice. This includes data about users, sla policies, groups, organizations, brands, and 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 AWS S3
:
pip install "dlt[filesystem]"
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 AWS S3
. You can run the following commands to create a starting point for loading data from HubSpot
to AWS S3
:
# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!
[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
2.1. Adjust the generated code to your usecase
By default, the filesystem destination will store your files as JSONL
. You can tell your pipeline to choose a different format with the loader_file_format
property that you can set directly on the pipeline or via your config.toml
. Available values are jsonl
, parquet
and csv
:
[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"
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='filesystem',
)
# 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='filesystem',
)
# 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='filesystem',
)
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='filesystem',
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 AWS S3
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: Learn how to automate your pipeline deployment using GitHub Actions.
- Deploy with Airflow: Follow the guide to deploy your pipeline using Airflow and Google Composer.
- Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions.
- Explore Other Deployment Options: Check out additional methods for deploying your pipeline here.
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 optimal performance and reliability. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified of any issues or changes in your
dlt
pipeline, allowing for proactive management and quick resolution of problems. Set up alerts - Set up tracing: Implement tracing in your
dlt
pipeline to gain detailed insights into its execution, helping you to diagnose issues and optimize performance. 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 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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