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Python Guide: Loading Data from hubspot to aws athena using dlt Library

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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 aws athena, an interactive query service from Amazon. The process involves using an open-source Python library called dlt. hubspot aids businesses in attracting visitors, engaging customers, and closing leads. On the other hand, aws athena offers an easy way to analyze data in Amazon S3 using standard SQL and supports iceberg tables. More information about hubspot can be found at https://www.hubspot.com. The dlt library serves as the bridge between these two platforms.

dlt Key Features

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage.

  • 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. Read more about adjusting a schema.

  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders, allowing them to take necessary actions.

  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. This includes running extraction, normalization and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and more. Read more about performance.

  • Community Support: dlt is a constantly growing library that supports many features and use cases needed by the community. You can join the community on Slack to find recent releases or discuss what you can build with dlt. Join the community here.

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 Athena:

pip install "dlt[athena]"

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

# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot athena
# 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[athena]>=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.athena]
query_result_bucket = "query_result_bucket" # please set me up!
athena_work_group = "athena_work_group" # please set me up!

[destination.athena.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

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 AWS Athena 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='athena',
)

# 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='athena',
)

# 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='athena',
)

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='athena',
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 Athena 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 CI/CD runner is free and allows you to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy dlt using Airflow. This tool creates an Airflow DAG for your pipeline script that you can customize, and it works with any Airflow instance.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This serverless execution environment allows you to run your code without managing any servers.
  • Other Deployment Options: There are many other ways you can deploy dlt. Check out this page for a full list of deployment options.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides tools to monitor your pipeline effectively and efficiently. Learn how to monitor your pipeline here.
  • Set Up Alerts: Stay updated on your pipeline's performance with dlt's alert system. Learn how to set up alerts here.
  • Set Up Tracing: dlt allows you to trace your pipeline's operations for better understanding and debugging. Learn how to set up tracing here.

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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