In this blog, we will see about Azure Data Factory and how to migrate or back up AWS S3 buckets to Azure Blob and schedule automatic triggers to take backups regularly in Azure Data Factory. We will connect Amazon S3 and Azure Blob using secret access and access and connection strings. We will also discuss Azure Data Factory and its features and benefits.
Azure Data Factory
Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and orchestrate data pipelines that move and transform data from various sources and destinations.
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Features of Azure Data Factory
- Data Integration: With Azure Data Factory, you can integrate data from various sources.
- Data Transformation: ADF provides data transformation capabilities, such as mapping fields, joining data, filtering data, and aggregating data.
- Data Movement: Azure Data Factory can move data from source systems to target systems, such as moving data from on-premises SQL Server databases to Azure Blob Storage or from Azure Blob Storage to Azure SQL Database.
- Data Orchestration: ADF allows you to orchestrate complex workflows that include multiple data sources, transformations, and destinations. You can schedule these workflows to run regularly, such as daily or weekly.
Benefits of Azure Data Factory
- Integration with Azure Services: ADF integrates with a wide range of Azure services, such as Azure Blob Storage, Azure SQL Database, Azure Data Lake Store, Azure HDInsight, Azure Databricks, and Azure Machine Learning. This integration makes it easy to incorporate these services into your data pipelines.
- Scalability and Flexibility: Azure Data Factory is designed to be scalable and flexible, allowing you to build data pipelines that can handle large amounts of data and grow as your data needs evolve. You can use ADF to process data in batch or real-time.
- Cost-Effective: Azure Data Factory is a cost-effective data integration solution with a pay-as-you-go pricing model. This means you only pay for what you use and can scale up or down as needed.
- Monitoring and Management: Azure Data Factory provides a range of monitoring and management capabilities, such as pipeline monitoring, activity monitoring, and logging. You can monitor the health of your data pipelines and troubleshoot issues as they arise.
Steps to Migrate Amazon S3 Bucket to Azure Blob Using Azure Data Factory
Step 1 – Create Amazon S3 Bucket and Azure Blob
Now we will create one Amazon S3 bucket and one folder inside the bucket and upload some image files.
Note: If your folder is empty, it will not be copied in Azure Blob.
We also need one Azure Blob container to take the backup.
We will create one Azure Data factory to launch the Azure Data factory studio.
Note: Create one storage account with Data Lake Storage Gen2 enabled and the rest as default.
Step 2 – Create a trigger to migrate the Amazon S3 bucket to Azure Blob
Our next step is to connect Azure blob and AWS S3 and create a trigger to migrate data.
To create a connection, we need the AWS account’s Access and Secret access keys and the Azure storage account’s Connection string. To get access keys and secret access keys, we need to generate them from the AWS IAM console.
Note: Access and secret keys are very sensitive credentials; do not share them with any unauthorized person.
Step 3 – Steps to create a trigger in the Azure data factory
- Create a Built-In copy task in Azure data studio and select the Schedule option to create the trigger.
2. Select Amazon S3 as the source type and click on +New Connection to create a connection with AWS S3.
3. Select Azure Data Lake Gen2 as the Destination type and, click on +New Connection, select Azure subscription from the Azure account list.
- Provide a valid Name and Description and click Next
Step 4 – Make sure all validation test is passed, and then click on the monitor
We have seen the migration of the Amazon S3 bucket to Azure Blob and also understand the functionality of Azure Data Factory. Azure Data Factory is a powerful data integration service that provides a range of features and benefits to help you build data pipelines that can move and transform data between different sources and destinations. With ADF, you can easily integrate data from various sources, transform the data using pre-built or custom transformations, move the data to different destinations, and orchestrate complex data workflows.
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Drop a query if you have any questions regarding Azure Data Factory and I will get back to you quickly.
1. Can we use SAS URI as an authentication type in Azure Data Factory?
ANS: – Yes, we can use multiple authentication types like service principle, SAS URI, and system assigned managed identities also.
2. Can we schedule the trigger at a specific time?
ANS: – Yes, we can schedule the trigger and make it recurrence every 24 hours.
WRITTEN BY Kishan Singh
Kishan Singh works as Research Associate (Infra, Migration, and Security) at CloudThat. He is Azure Administrator and Azure Developer certified. He is highly organized and an excellent communicator with good experience in Cyber Security and Cloud technologies. He works with a positive attitude and has a good problem-solving approach.