Loading Salesforce Data to Supabase Using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Supabase. You can get the connection string for your Supabase database as described in the Supabase Docs.

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

Loading data from Salesforce to Supabase can be streamlined using the open source Python library dlt. Salesforce is a cloud platform that enhances business operations and customer relationship management, covering sales, marketing, and customer service. On the other hand, Supabase is an open source Firebase alternative, providing a Postgres database, authentication, instant APIs, edge functions, real-time subscriptions, storage, and vector embeddings. This documentation will guide you through the process of using dlt to efficiently transfer data from Salesforce to Supabase. For more information on Salesforce, visit salesforce.com.

dlt Key Features

• Automated maintenance: dlt simplifies maintenance with schema inference, evolution, and alerts, making it easier to manage pipelines. Learn more
• Run it where Python runs: Whether on Airflow, serverless functions, or notebooks, dlt works without requiring external APIs, backends, or containers. Learn more
• User-friendly interface: dlt features a declarative interface that is accessible for beginners and powerful for experienced users. Learn more
• Robust governance support: dlt offers metadata utilization, schema enforcement, and schema change alerts to ensure data governance. Learn more
• Scalability and performance: dlt allows parallel execution of extraction, normalization, and load processes, as well as fine-tuning of memory buffers and file sizes. 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 Supabase:

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

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

simple-salesforce>=1.12.4
dlt[postgres]>=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.postgres]
dataset_name = "dataset_name" # 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

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 Supabase 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='postgres', 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 Supabase 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: Utilize GitHub Actions for CI/CD to automate your dlt pipeline deployment with a cron schedule.
• Deploy with Airflow and Google Composer: Follow the guide on deploying with Airflow and Google Composer to manage your dlt pipelines.
• Deploy with Google Cloud Functions: Learn how to deploy your dlt pipeline using Google Cloud Functions for serverless execution.
• Explore other deployment options: Check out additional deployment methods for dlt pipelines, including various cloud services and CI/CD tools.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production with detailed insights and metrics. How to Monitor your pipeline
• Set up alerts: Ensure you are promptly notified of any issues or important events in your dlt pipeline by setting up alerts. Set up alerts
• Set up tracing: Implement tracing to get a comprehensive view of your pipeline's execution and performance, enabling easier debugging and optimization. And set up tracing

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

Additional pipeline guides

• Load data from Rest API to Azure Cloud Storage in python with dlt
• Load data from Attio to BigQuery in python with dlt
• Load data from Spotify to AlloyDB in python with dlt
• Load data from SAP HANA to Azure Cosmos DB in python with dlt
• Load data from Braze to BigQuery in python with dlt
• Load data from Notion to Dremio in python with dlt
• Load data from Bitbucket to YugabyteDB in python with dlt
• Load data from GitLab to DuckDB in python with dlt
• Load data from Pinterest to EDB BigAnimal in python with dlt
• Load data from Imgur to Redshift in python with dlt