Python Data Loading from salesforce to mssql with dlt

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.

This page provides technical documentation on how to load data from Salesforce, a cloud platform that enhances business operations and customer relationship management, to Microsoft SQL Server, a relational database management system (RDBMS). The process utilizes an open-source python library called dlt. Salesforce unifies sales, marketing, and customer service, while Microsoft SQL Server allows applications and tools to connect and communicate using Transact-SQL. For more detailed information on Salesforce, please visit https://www.salesforce.com/. The dlt library simplifies and streamlines the data transfer process between these two powerful platforms.

dlt Key Features

• Automated Maintenance: With features such as schema inference and evolution and alerts, dlt ensures that maintenance becomes simple and automated. Learn more here.
• Versatility: dlt can run wherever Python runs. This includes environments like Airflow, serverless functions, and notebooks. It does not require external APIs, backends, or containers, and can scale on both micro and large infrastructures. Learn more here.
• User-friendly Interface: dlt provides a user-friendly, declarative interface that is easy for beginners to understand and powerful for senior professionals to use. Learn more here.
• Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This contributes to better data management practices, compliance adherence, and overall data governance. Learn more here.
• Support for Multiple Data Sources and Destinations: dlt supports a wide range of data sources and destinations, including popular platforms like Salesforce, Airtable, and Microsoft SQL Server. Learn more here and 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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce mssql
# 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[mssql]>=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.mssql.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 Salesforce source in our docs.
• Read more about setting up the Microsoft SQL Server 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='mssql', 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 Microsoft SQL Server 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 is a CI/CD runner that you can use for free. You need to specify when the GitHub Action should run using a cron schedule expression.
• Deploy with Airflow: You can deploy a pipeline with dlt using Airflow. This method uses Google Composer, a managed Airflow environment provided by Google.
• Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
• Other Deployment Options: dlt offers a variety of other deployment options. You can explore more about these on the Deployment Walkthroughs page.

The running in production section will teach you about:

• Monitor Your Pipeline: With dlt, you can easily monitor your data pipeline. It provides detailed insights into the status and performance of your pipeline. Learn more about how to monitor your pipeline here.
• Set Up Alerts: dlt allows you to set up alerts that will notify you when something goes wrong with your pipeline. This feature ensures that you can quickly react to any issues and keep your pipeline running smoothly. Learn how to set up alerts here.
• Set Up Tracing: Tracing is a powerful feature that allows you to track the execution of your pipeline and identify any potential bottlenecks or issues. dlt makes it easy to set up tracing for your pipeline. Learn how to set up tracing here.

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 MongoDB to MotherDuck in python with dlt
• Load data from Pipedrive to AlloyDB in python with dlt
• Load data from Slack to DuckDB in python with dlt
• Load data from IBM Db2 to Azure Cloud Storage in python with dlt
• Load data from Google Cloud Storage to Google Cloud Storage in python with dlt
• Load data from Klaviyo to The Local Filesystem in python with dlt
• Load data from Sentry to Microsoft SQL Server in python with dlt
• Load data from Mux to Azure Cosmos DB in python with dlt
• Load data from IBM Db2 to Azure Cosmos DB in python with dlt
• Load data from AWS S3 to Snowflake in python with dlt