Python Data Loading from salesforce to azure synapse using dlt

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This documentation provides a guide on how to load data from Salesforce, a cloud platform that optimizes business operations and customer relationship management, into Azure Synapse, an expansive analytics service that combines enterprise data warehousing and Big Data analytics. This process is accomplished using the open-source Python library, dlt. The dlt library facilitates efficient data transfer between Salesforce and Azure Synapse, simplifying the complex task of data migration and manipulation. For more details on Salesforce, visit https://www.salesforce.com/.

dlt Key Features

• Automated Maintenance: dlt offers automated maintenance with schema inference and evolution and alerts. It also uses short, declarative code to simplify maintenance tasks. Learn more
• Run Anywhere: dlt is designed to run wherever Python runs. It can be used on Airflow, serverless functions, notebooks, and more. It does not require external APIs, backends, or containers, and it can scale on micro and large infrastructures alike. Learn more
• User-friendly Interface: dlt has a user-friendly, declarative interface that is easy for beginners to learn, but also powerful enough for senior professionals to use effectively. Learn more
• Powerful Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more
• Community Support: The dlt community is a great resource for getting help and sharing knowledge. You can join the community on Slack, give the library a star on GitHub, ask questions, and report problems or make feature requests. 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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 Azure Synapse 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='synapse', 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 Azure Synapse 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

• 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.
• Airflow: You can deploy dlt with Airflow. This guide will help you create an Airflow DAG for your pipeline script that you should customize.
• Google Cloud Functions: dlt can also be deployed using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
• And others...: There are other ways to deploy dlt as well. You can find more information on the deployment page.

The running in production section will teach you about:

• Monitor your pipeline: dlt provides a robust monitoring system that allows you to keep track of your pipeline's performance and status. You can easily inspect and save load info, trace runtime, and even alert on schema changes. Learn more about how to monitor your pipeline here.
• Set up alerts: Stay on top of your pipeline's health by setting up alerts with dlt. This feature allows you to receive notifications whenever there are significant changes or issues with your pipeline. Find out how to set up alerts here.
• Set up tracing: Tracing is a crucial aspect of running a pipeline in production. With dlt, you can get detailed insights into your pipeline's operation, allowing you to identify and resolve issues quickly. 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

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