Case Study: Salesforce Data Extractionusing Azure Data Factory

Industry: All

Solution: Salesforce Data extraction using ADF.

Background

A mid-sized retail company with a rapidly expanding customer base relied heavily on Salesforce

as their primary CRM platform. The company wanted to integrate Salesforce data into their

centralized data warehouse on Azure for advanced analytics, reporting, and machine learning

initiatives. The existing process of manual data export was inefficient and error-prone, lacking

scalability and real-time insights.

To streamline operations and improve data-driven decision-making, the organization chose

Azure Data Factory (ADF) to automate and orchestrate the extraction, transformation, and

loading (ETL) of Salesforce data.

Challenges

1. Complex Salesforce Schema: Salesforce's data model included numerous objects,

   nested relationships, and custom fields, which made it difficult to map and extract the

   necessary data.

2. API Limitations: Salesforce imposes limits on API usage (calls/day and concurrency),

   which could disrupt data extraction during high-volume operations.

3.  Data Volume & Latency: Large volumes of data, especially historical data, required an

   efficient extraction strategy to prevent performance bottlenecks.

4.  Incremental Load: The company needed a mechanism to extract only the changed data

   (CDC) to optimize performance and avoid duplicate processing.

5. Security & Compliance: Ensuring secure data transmission and storage was essential to

   meet industry regulations and internal data governance policies.

Solution

The organization implemented Azure Data Factory (ADF) as the ETL tool to extract data from

Salesforce and load it into Azure SQL Database for analytics. The architecture leveraged ADF

Salesforce connector, pipelines, dataflows, and parameterization to create a scalable,

automated integration solution.

Key components of the solution included:

• ADF Salesforce Connector for seamless API integration

• Parameterized Pipelines for reusability across multiple Salesforce objects

• Incremental Loading Logic using SystemModstamp

• Data Mapping & Transformation using Data Flows

• Logging & Monitoring with Azure Monitor and ADF integration runtime logs

Implementation Process

Step 1: Requirements Gathering

• Identified key Salesforce objects (Accounts, Contacts, Opportunities, Leads, Custom

  Objects)

• Defined data refresh frequency (daily full loads for initial, incremental thereafter)

Step 2: ADF Setup

• Created Linked Services:

  • Salesforce (OAuth authentication)

  • Azure Datalakes

• Configured Integration Runtime (IR) for data movement

Step 3: Pipeline Design

• Created reusable pipelines with parameters (object name, modified date, service name,

  query)

• Implemented lookup activities to get last successful load timestamp

• Used conditional split to handle full vs incremental loads

Step 4: Data Extraction

• Used ADF’s built-in Salesforce source connector

• For incremental loads, applied filters using SystemModstamp > last_loaded_date

• Stored extracted data in staging tables in Azure Datalakes

Step 5: Data Transformation & Load

• Used Data Flows to:

  • Clean nulls and duplicates

  • Map columns from source to target

  •  Enrich data with additional metadata

• Loaded transformed data into the final reporting tables

Step 6: Logging & Alerts

• Implemented custom logging tables

• Configured failure alerts using Azure Monitor and Logic App

Result

The new integration architecture achieved the following outcomes:

• Automation: Reduced manual effort by 90% through scheduled data pipelines.

• Performance: Improved data load performance with incremental loading and parallel

  processing.

• Data Freshness: Enabled near real-time reporting by refreshing data hourly.

• Scalability: Easily added new Salesforce objects to the pipeline with minimal effort.

• Compliance: Ensured secure, encrypted data transfer and role-based access controls.

Conclusion

By leveraging Azure Data Factory, the company successfully automated the extraction of

complex Salesforce data into Azure for analytics. The solution not only improved operational

efficiency but also empowered business users with timely, accurate insights. The use of

incremental load strategies, secure architecture, and robust monitoring ensured a production-

grade solution that could scale with growing data needs.