Скачать или смотреть How to Efficiently Extract, Transform, and Load Data Between Azure SQL Databases

Discover easy and efficient ways to `copy`, transform, and move data from one Azure SQL Database to another while maintaining data integrity and security.
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Introduction

When working with cloud-based databases in Azure, transferring data between instances can present a few challenges, especially when the integrity and anonymity of that data are paramount. In this guide, we'll address a common situation: copying data from an Azure SQL Database in a production environment to another Azure SQL Database in a QA environment. This needs to happen in a secure manner, with the data undergoing necessary transformations to anonymize sensitive information. Let’s delve into how you can achieve this using Azure Data Factory.

The Challenge

Imagine you have two Azure SQL databases:

Production Database: This is where your live data resides.

QA Database: This serves as your testing ground for new features and data handling processes.

You need to:

Transfer data from the production database to the QA database regularly (e.g., every week).

Anonymize data before it is loaded into QA.

Avoid deleting and recreating databases, especially since deletion locks are in place.

The solution to this challenge lies in leveraging Azure Data Factory (ADF), a robust cloud-based data integration service.

Solution: Using Azure Data Factory

Azure Data Factory is an ideal choice for your requirements. Here’s how you can set it up effectively to handle data transfers and transformations:

Setting Up Azure Data Factory

Create an Azure Data Factory Instance:

Go to the Azure Portal.

Click on "Create a resource" and select "Data + Analytics" "Data Factory".

Follow the on-screen prompts to set it up in your desired resource group.

Create Linked Services:

Production SQL Database: Create a linked service that connects to your production database.

QA SQL Database: Create a second linked service for your QA database.

Data Flow Design

Create a Pipeline:

In Azure Data Factory, navigate to "Author & Monitor" and create a new pipeline.

Drag and drop the “Copy Data” activity from the activities pane.

Configuring the Copy Activity:

Select the source as your Production SQL Database linked service.

Select the destination as your QA SQL Database linked service.

Specify the table(s) you want to copy or define a custom query if simple transformations are required.

Transform Data:

Within the pipeline, you can add data transformation steps using mapping data flows, which allow you to apply functions to anonymize sensitive data fields.

Scheduling the Pipeline

Set a trigger for your pipeline to run on a weekly schedule. This can be done by selecting the "Add Trigger" option and choosing "New/Edit".

Specify the frequency and time for the execution, ensuring that the QA database is updated regularly.

Cleaning Up the Target Database

Since you want to drop existing data in the QA database each run, you can add a Stored Procedure activity to your pipeline that clears the relevant tables before the copy operation starts.

Conclusion

Using Azure Data Factory, you can efficiently manage the extraction, transformation, and loading of data between your Azure SQL databases. This approach provides a streamlined method to maintain data integrity and ensure that sensitive information is adequately anonymized before entering the QA environment.

By following the steps outlined above, you can efficiently schedule regular updates, ensuring your QA database is always populated with fresh, anonymized data from production. This enables your QA team to perform testing and validation with the most relevant data, without compromising security or integrity.

Now that you understand how to seamlessly move data between Azure SQL databases while transforming it along the way, it’s time to implement Azure Data Factory and enhance your data management proce