Master Multi-Cloud Data Integration with Azure Data Factory: A Step-by-Step Journey

Introduction

As an aspiring Data Engineer, I worked on a mini-project that tackles a common challenge: integrating data across multiple cloud platforms. Businesses often store data in different cloud environments, making unified analysis difficult. In this project, I used Azure Data Factory (ADF) to seamlessly move data from AWS S3 and Azure Blob Storage to Staging to Snowflake, enabling multi-cloud data integration for analytics.

This article covers the step-by-step implementation, key takeaways, and why this approach is valuable for real-world data engineering workflows.

Problem Statement

Modern enterprises use various cloud platforms to store structured and unstructured data. However, integrating data from different cloud providers is often complex due to differences in storage formats, access controls, and connectivity.

Objective

To integrate customer and order data from AWS S3 and Azure Blob Storage → Staging and then into Snowflake using Azure Data Factory (ADF) for business analysis.

Data Sources

• AWS S3 Bucket: Contains customers.csv (Customer data)
• Azure Blob Storage: Contains orders.csv (Order data)
• Snowflake Warehouse: Target analytical database
• Azure Data Factory (ADF): ETL orchestration tool

Step-by-Step Implementation

Step 1: Prerequisites

Before starting, ensure you have:

? AWS S3 Bucket (customers-data-s3-bucket with customers.csv file)

? Azure Blob Storage (Container: orders with orders.csv file)

? Snowflake Account (Tables: customers and orders)

? Azure Data Factory (ADF) (For data movement and orchestration)

? IAM User for AWS S3 (With AmazonS3ReadOnlyAccess policy)

Step 2: Setting Up AWS S3

1. Create S3 Bucket: In AWS console, navigate to S3, click Create Bucket, name it (customers-data-s3-bucket), and disable public access.
2. Upload File: Click the bucket, select Upload, add customers.csv, and confirm upload.
3. Create IAM User & Generate Access Keys:

Step 3: Setting Up Azure Blob Storage

1. Create Storage Account (awsazstorageac)
2. Create Container (orders)
3. Upload File (orders.csv into orders container)

Step 4: Setting Up Snowflake

1. Create Database & Schema:

CREATE DATABASE SALES_DB;        

CREATE SCHEMA SALES_DB.SALES_SCHEMA;        

1. Create Tables:

CREATE TABLE SALES_DB.SALES_SCHEMA.CUSTOMERS(
    CUSTOMER_ID NUMBER(38, 0) NOT NULL PRIMARY KEY,
    FIRST_NAME VARCHAR(33),
    LAST_NAME VARCHAR(33),
    EMAIL VARCHAR(55),
    PHONE VARCHAR(12),
    ADDRESS VARCHAR(55)
);        

CREATE TABLE SALES_DB.SALES_SCHEMA.ORDERS(
    ORDER_ID NUMBER(38, 0) NOT NULL PRIMARY KEY,
    CUSTOMER_ID NUMBER(38, 0),
    ORDER_DATE DATE,
    TOTAL_AMOUNT FLOAT,
    ORD_QTY NUMBER(5, 0)
);        

Step 5: Configuring Linked Services in ADF

AWS S3 Connection:

• Go to Azure Data Factory → Manage → Linked Services → New → Search Amazon S3, → enter Access Key & Secret Key → Test and Save.

Azure Blob Storage Connection:

• Create a new linked service for Azure Blob Storage

Snowflake Connection:

• Add Snowflake linked service, enter account details, test, and save.

Step 6: Creating Datasets in ADF

1. Amazon S3 Dataset (Delimited Text for customers.csv)
2. Azure Blob Storage Dataset (Delimited Text for orders.csv)
3. Snowflake Dataset (Tables: Customers and Orders)

Step 7: Creating ADF Pipeline

1. Create Pipeline (S3_n_Azure_Blob_to_Snowflake)
2. Add Copy Data Activities:

Source: AWS S3 (customers.csv) → Sink: Snowflake Customers table

Source: Azure Blob (orders.csv) → Sink: Snowflake Orders table

Enable Staging for Performance:

• Create staging container in Azure Blob Storage
• Generate SAS Token for authentication
• Configure staging in ADF pipeline settings
• Publish & Test Pipeline

Step 8: Scheduling with ADF Triggers

• Navigate to ADF Studio → Author → Select Pipeline → Add Trigger → Configure Schedule → Publish & Test

Step 9: Verify Data in Snowflake

Run SQL queries to check if data has been loaded correctly:

SELECT * FROM SALES_DB.SALES_SCHEMA.CUSTOMERS;
SELECT * FROM SALES_DB.SALES_SCHEMA.ORDERS;          

Key Takeaways & Learnings

? Cross-cloud data integration is seamless with ADF

? IAM roles & access permissions are critical for security

? Staging improves performance in large data loads

? Snowflake’s ELT approach optimizes analytical queries

Why This Matters?

?? Multi-cloud is the future – Companies increasingly use multiple cloud vendors. Efficient data integration across clouds is a must-have skill for Data Engineers.

? ADF simplifies orchestration – With minimal coding, it enables robust ETL pipelines for cloud-scale data movement.

?? Better decision-making – Unified data in Snowflake enables advanced analytics, reporting, and business intelligence.

Conclusion

This project demonstrates how Azure Data Factory (ADF) can be leveraged for cross-cloud data integration. By moving data from AWS S3 & Azure Blob → Staging and then into → Snowflake, we unlocked seamless data analytics and reporting across platforms.

As an #Data #Engineer, I find this hands-on experience incredibly valuable in understanding #ETL, #Data #Orchestration, and #Cloud #Integration.

Let’s Connect!

If you found this project interesting or have questions about cross-cloud data integration, feel free to reach out! I’d love to connect, share insights, and learn from your experiences. Let’s keep pushing the boundaries of what’s possible with data engineering! ??

#DataEngineering #AzureDataFactory #Snowflake #CloudIntegration #DataIntegration #DataAnalytics #AWS #Azure #DataPipeline #TechJourney

What do you think? Have you worked on similar projects? Let’s discuss in the comments! ??

P.S. If you’re interested in the step-by-step guide or want to see the code, feel free to DM me or visit the GitHub repo.

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