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Loading Data from salesforce to redshift Using dlt in Python

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This technical document outlines how to use the open-source Python library, dlt, to transport data from Salesforce, a cloud platform for business operations and customer relationship management, to Redshift, a fully managed, petabyte-scale data warehouse service in the cloud. The dlt library simplifies this process, making it an efficient solution for managing and migrating data. For more information about Salesforce, please visit https://www.salesforce.com/.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance with schema inference, evolution and alerts. It uses short, declarative code, making maintenance simple and straightforward. Learn More
  • Scalability and Flexibility: dlt can run wherever Python runs - on Airflow, serverless functions, notebooks, and more. It doesn't require external APIs, backends, or containers, and scales on both micro and large infrastructure alike. Learn More
  • Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Learn More
  • Efficient Data Extraction: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn More
  • Community Support: dlt has a growing community 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

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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce redshift
# 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[redshift]>=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.redshift.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 = 5439
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 Redshift 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='redshift', 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 Redshift 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​

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides extensive monitoring capabilities for your data pipeline. It allows you to track and analyze the performance of your pipeline, helping you identify and fix any potential issues. Check out the monitoring guide to learn more.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any significant changes or anomalies in your pipeline. This feature ensures that you are always aware of the status of your pipeline. Learn how to set up alerts with the alerting guide.
  • Enable Tracing: dlt allows you to enable tracing in your pipeline. This feature provides detailed insights into the execution of your pipeline, helping you to identify bottlenecks and improve performance. Learn how to set up tracing with the 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 NameWrite DispositionDescription
accountmergeRepresents an individual or organization that interacts with your business
campaignreplaceRepresents a marketing initiative or project designed to achieve specific goals
contactreplaceRepresents an individual person associated with an account or organization
leadreplaceRepresents a prospective customer/individual/org. that has shown interest in a company's products/services
opportunitymergeRepresents a sales opportunity for a specific account or contact
pricebook_2replaceUsed to manage product pricing and create price books
pricebook_entryreplaceRepresents a specific price for a product in a price book
product_2replaceUsed for managing and organizing your product-related data within the Salesforce ecosystem
sf_userreplaceRepresents an individual who has access to a Salesforce org or instance
user_rolereplaceRepresents a role within the organization's hierarchy

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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