Python Data Loading from salesforce to aws athena using dlt Library

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This documentation provides guidance on how to load data from salesforce, a cloud-based platform that optimizes business operations and customer relationship management, to aws athena, an interactive query service that simplifies data analysis in Amazon S3 using standard SQL. The process utilizes an open-source Python library, dlt, which supports iceberg tables. For more information on salesforce, visit https://www.salesforce.com/. The document will walk you through the steps of using dlt to effectively transfer data from salesforce to aws athena.

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 here.

• 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. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more about Adjust a schema here.

• 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 about performance here.

• Scaling and Finetuning: dlt offers several mechanism and configuration options to scale up and fine-tune pipelines: Running extraction, normalization and load in parallel, Writing sources and resources that are run in parallel via thread pools and async execution, Finetune the memory buffers, intermediary file sizes and compression options. Read more about performance here.

• Community Support: dlt is a constantly growing library that supports many features and use cases needed by the community. You can join the Slack community to find recent releases or discuss what you can build with dlt.

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 AWS Athena:

pip install "dlt[athena]"

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

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

[destination.athena.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 AWS Athena 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='athena', 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 AWS Athena 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 a simple and straightforward way to deploy your pipelines using Github Actions. You can specify when the GitHub Action should run using a cron schedule expression.
• Deploy with Airflow and Google Composer: You can also deploy your dlt pipelines using Airflow and Google Composer. This method is ideal if you are already using Google Composer as a managed Airflow environment.
• Deploy with Google Cloud Functions: If you prefer to use serverless architecture for your pipelines, dlt supports deployment using Google Cloud Functions. This is a great way to run your pipelines without the need for managing servers.
• Other Deployment Options: In addition to the above, dlt offers a variety of other deployment options. You can check out all the available options for deploying your dlt pipelines here.

The running in production section will teach you about:

• Monitor your pipeline: dlt provides comprehensive tools for monitoring your pipeline to ensure it's operating as expected. You can keep track of the pipeline's performance, identify any issues, and take corrective action if necessary. For more information, visit How to Monitor your pipeline.
• Set up alerts: With dlt, you can set up alerts to notify you about any important events or issues in your pipeline. This allows you to respond quickly to any potential problems and ensure your pipeline continues to run smoothly. Learn more about this at Set up alerts.
• Implement tracing: Tracing is a powerful feature in dlt that allows you to track the execution of your pipeline and understand its behavior. This can help you optimize your pipeline and identify any potential bottlenecks. Check out Set up tracing for more details.

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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