Python Data Loading from salesforce to postgresql using dlt

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This documentation provides information on how to use the open-source Python library dlt to load data from Salesforce, a cloud platform that optimizes business operations and customer relationship management, into PostgreSQL, an open-source object-relational database system known for its robustness and scalability. Salesforce integrates various business aspects such as sales, marketing, and customer service, while PostgreSQL extends the SQL language to handle complex data workloads efficiently. The dlt library facilitates this data transfer, providing a seamless link between these two powerful platforms. For more details on Salesforce, please visit https://www.salesforce.com/.

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: Adjust a schema docs.
• Schema evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations, dlt notifies stakeholders, allowing them to take necessary actions. Read more here.
• Scaling and finetuning: dlt offers several mechanism and configuration options to scale up and finetune pipelines. It supports running extraction, normalization and load in parallel. Read more about performance.
• Postgres Destination: dlt provides a destination for Postgres, allowing for easy data loading and management. It supports all write dispositions and integrates with dbt. Read more 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 PostgreSQL:

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

# 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.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 PostgreSQL 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 PostgreSQL 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 involves setting up a GitHub action to run your pipeline at specified intervals.
• Deploy with Airflow: You can also deploy dlt with Airflow. This involves setting up a managed Airflow environment, such as Google Composer, to run your pipeline.
• Deploy with Google Cloud Functions: dlt pipelines can be deployed as Google Cloud Functions. This involves packaging your pipeline as a cloud function and deploying it to Google Cloud.
• Other Deployment Options: There are several other ways to deploy dlt. You can find more information on these methods here.

The running in production section will teach you about:

• Monitor Your Pipeline: It's essential to keep an eye on your pipeline's performance and status. dlt provides tools to help you monitor your pipeline effectively. Check out the guide on how to monitor your pipeline.
• Set Up Alerts: To ensure your pipeline runs smoothly, setting up alerts can be beneficial. Alerts notify you of any issues or changes that might affect your pipeline's performance. Learn more about how to set up alerts.
• Set Up Tracing: Tracing allows you to track the execution of your pipeline and identify any potential bottlenecks or errors. This can be crucial for optimizing your pipeline and ensuring its reliability. Find out more about setting 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

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