Python Data Loading from salesforce to bigquery with dlt Library

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This page provides technical documentation on how to utilize the open-source Python library, dlt, to load data from Salesforce to BigQuery. Salesforce is a cloud platform that streamlines business operations and customer relationship management, including sales, marketing, and customer service. On the other hand, BigQuery is a serverless, cost-effective enterprise data warehouse that scales with your data and operates across clouds. The dlt library serves as the bridge, facilitating the transfer of data between these two platforms. For more information about Salesforce, you can visit https://www.salesforce.com/.

dlt Key Features

• Automated Maintenance: dlt offers automated maintenance through schema inference and evolution and alerts. It requires short declarative code, making maintenance simple and straightforward. Learn more
• Run Anywhere: dlt can run anywhere Python runs - on Airflow, serverless functions, notebooks, etc. It does not require external APIs, backends, or containers, and can scale on both micro and large infrastructures. Learn more
• User-friendly Interface: dlt provides a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
• Google BigQuery Destination: dlt supports Google BigQuery as a destination. This guide provides instructions on how to set up dlt with BigQuery dependencies, create a new Google Cloud project, create a service account and grant BigQuery permissions, and update your dlt credentials file with your service account info. Learn more
• Data Lineage: dlt provides data lineage tracing, allowing you to track the origin and transformation of your data. This helps ensure the integrity and reliability of your data. 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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce bigquery
# 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:

simple-salesforce>=1.12.4
dlt[bigquery]>=0.3.5

You now have the following folder structure in your project:

salesforce_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── salesforce/                   # folder with source specific files
│   └── ...
├── salesforce_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.salesforce]
user_name = "user_name" # please set me up!
password = "password" # please set me up!
security_token = "security_token" # please set me up!

[destination.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 Salesforce source in our docs.
• Read more about setting up the BigQuery 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 salesforce_pipeline.py, as well as a folder salesforce 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:

#!/usr/bin/env python3
"""Pipeline to load Salesforce data."""
import dlt
from salesforce import salesforce_source


def load() -> None:
    """Execute a pipeline from Salesforce."""

    pipeline = dlt.pipeline(
        pipeline_name="salesforce", destination='bigquery', dataset_name="salesforce_data"
    )
    # Execute the pipeline
    load_info = pipeline.run(salesforce_source())

    # Print the load info
    print(load_info)


if __name__ == "__main__":
    load()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python salesforce_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline salesforce info

You can also use streamlit to inspect the contents of your BigQuery destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline salesforce 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 approach allows you to automate your workflows and run tasks in response to specific events on GitHub.
• Deploy with Airflow: For those who prefer using Airflow, dlt provides a guide on how to deploy a pipeline with Airflow and Google Composer. This guide will help you create an Airflow DAG for your pipeline script.
• Deploy with Google Cloud Functions: If you're using Google Cloud, you can deploy your dlt pipelines using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
• Other Deployment Options: In addition to the above, dlt supports other deployment options. You can find more information on how to deploy a pipeline in the official documentation.

The running in production section will teach you about:

• Monitor Your Pipeline: With dlt, you can easily monitor your pipeline's performance and keep track of its status. Check out the guide on how to monitor your pipeline for more details.
• Set Up Alerts: dlt allows you to set up alerts to notify you of any potential issues or changes in your pipeline. Learn more about setting up alerts in the Set up alerts guide.
• Set Up Tracing: Tracing is an essential feature of dlt that helps you track the execution of your pipeline and identify any potential issues. Learn more about 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 salesforce

"Salesforce source provides comprehensive business data, covering customer details, sales opportunities, product pricing, and marketing campaigns."

Resource Name	Write Disposition	Description
account	merge	Represents an individual or organization that interacts with your business
campaign	replace	Represents a marketing initiative or project designed to achieve specific goals
contact	replace	Represents an individual person associated with an account or organization
lead	replace	Represents a prospective customer/individual/org. that has shown interest in a company's products/services
opportunity	merge	Represents a sales opportunity for a specific account or contact
pricebook_2	replace	Used to manage product pricing and create price books
pricebook_entry	replace	Represents a specific price for a product in a price book
product_2	replace	Used for managing and organizing your product-related data within the Salesforce ecosystem
sf_user	replace	Represents an individual who has access to a Salesforce org or instance
user_role	replace	Represents a role within the organization's hierarchy

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