Python Data Loading from salesforce to dremio with dlt Library

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This technical documentation provides a guide on how to load data from salesforce, a cloud platform that optimizes business operations and customer relationship management, to dremio, a data lakehouse solution offering flexibility, scalability, and performance at all stages of data journey. The process is facilitated using dlt, an open-source Python library. The documentation offers step-by-step instructions on how to effectively use dlt for data transfer between salesforce and dremio. For more information about salesforce, please visit https://www.salesforce.com/.

dlt Key Features

• Automated Maintenance: dlt offers automated maintenance through schema inference and evolution and alerts, making maintenance simple and straightforward. Check out the introductory guide to learn more.
• Scalability and Performance: dlt provides scalability through iterators, chunking, and parallelization, making it efficient for handling large datasets. It also offers several mechanisms and configuration options to fine-tune pipelines. Learn more about performance.
• Data Extraction: Extracting data with dlt is simple and efficient, leveraging iterators and implicit extraction DAGs for effective and optimized data extraction. Learn more about building a pipeline.
• Post-Loading Transformations: dlt provides several options for data transformations after loading, including using dbt, the dlt SQL client, or Pandas. Learn more about these options in the build a pipeline tutorial.
• Community Support: dlt has a robust community that provides support and shares how they use the library. You can ask questions, report problems, and make feature requests. Join the dlt community today.

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 Dremio:

pip install "dlt[dremio]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce dremio
# 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[dremio]>=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.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.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 = 32010

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 Dremio 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='dremio', 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 Dremio 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 provides an easy way to deploy your pipelines using Github Actions. This method is essentially free and can be scheduled using a cron schedule expression.
• Deploy with Airflow: With dlt, you can deploy your pipelines using Airflow. This method is especially useful if you are using Google Composer, a managed Airflow environment provided by Google.
• Deploy with Google Cloud Functions: dlt also supports deployment of pipelines using Google Cloud Functions. This method is beneficial if you want to run your pipelines in a serverless environment.
• Other Deployment Methods: dlt supports a variety of other deployment methods. You can find more information on these methods here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides a robust monitoring system for your data pipeline. You can keep track of your pipeline's performance and troubleshoot any issues that arise. Learn more about it here.
• Set Up Alerts: Stay informed about your pipeline's status with dlt's alerting feature. It allows you to set up notifications for specific events or conditions in your pipeline. Find out how to set up alerts here.
• Implement Tracing: dlt offers tracing capabilities that allow you to track the execution of your pipeline and identify potential bottlenecks or failures. 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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