Mastering Azure Migration: A Brief Guide to Rehost, Rearchitect, Refactor, and Rebuild

Azure Migration is a pivotal step for organizations seeking enhanced scalability, flexibility, and innovation. This article provides a concise overview of migration approaches—Rehost, Rearchitect, Refactor, and Rebuild—offering insights into choosing the right strategy for your unique needs.

1. Rehost: "Lift and Shift"

• Description: Move existing applications to Azure without modifying their structure.

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• Benefits:

  • Quick migration.
  • Minimal changes to existing code.

• Use Cases:

• Legacy applications.
• Urgent need for cloud presence.

• 2. Rearchitect: "Adopt Cloud"

• Description: Optimize existing applications for better performance and monitoring in Azure.

• Benefits:

  • Minimal changes to existing code.
  • Better scalability & monitoring

• Use Cases:

  • Applications with performance bottlenecks.
  • Urgent need for cloud presence.

3. Refactor: "Optimize for the Cloud"

• Description: Optimize existing applications for better performance and scalability in Azure.

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• Benefits:

  • Improved performance.
  • Better scalability.

• Use Cases:

  • Applications with performance bottlenecks.
  • Enhancing scalability for varying workloads.

4. Rebuild: "Cloud-Native Transformation"

• Description: Redesign and rebuild applications using cloud-native architectures.

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• Benefits:

  • Fully leverage Azure services.
  • Optimal performance and scalability.

• Use Cases:

  • Modernization of applications.
  • Greenfield projects for cloud-native development.

Conclusion:

Choose the right migration approach based on your goals, existing infrastructure, and future needs. Rehost for a quick entry, Refactor for optimization, and Rebuild for cloud-native innovation. Azure Migration empowers businesses to embrace the cloud seamlessly, ensuring a future-ready and resilient IT landscape.

When to select Azure Data Factory vs Azure Synapse Analytics?

Both Data Factory and Synapse analytics (Previously known as SQL warehouse) are popular for ELT operations. but its always a challenge to understand what to select based on your need. 

Below is the list of features supported in Azure Data Factory vs Azure Synapses Analytics :

Category	Feature	Azure Data Factory	Azure Synapse Analytics
Integration Runtime	Support for Cross-region Integration Runtime (Data Flows)	✓	✗
	Integration Runtime Sharing	✓ Can be shared across different data factories	✗
Pipelines Activities	Support for Power Query Activity	✓	✗
	Support for global parameters	✓	✗
Template Gallery and Knowledge center	Solution Templates	✓ Azure Data Factory Template Gallery	✓ Synapse Workspace Knowledge center
GIT Repository Integration	GIT Integration	✓	✓
Monitoring	Monitoring of Spark Jobs for Data Flow	✗	✓ Leverage the Synapse Spark pools

Demystifying Data Integration approach: ETL vs. ELT

 

Introduction:

In the realm of data integration, choosing the right approach is critical for efficient and effective processing. Azure Data Factory and Synapse Analytics provide powerful tools for managing data workflows, and understanding the distinctions between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) methodologies is essential.

1. 1. ETL Scenario - Data Warehousing:

      - Context: A retail company collects sales data from various online and in-store channels.

      - Use Case: ETL is ideal when transforming and aggregating this diverse sales data into a structured format for storage in a centralized data warehouse. This allows for easy reporting and analytics, enabling business leaders to make informed decisions based on consolidated sales insights.

   
   

   2. ETL Scenario - Schema Transformation:

      - Context: An insurance company merges with another, each using different data schemas for customer information.

      - Use Case: ETL is crucial in this scenario to harmonize and transform the disparate data schemas into a unified format before loading into a consolidated database. This ensures a seamless transition and accurate reporting across the merged entities.

   
   

   3. ETL Scenario - Source System Aggregation:

      - Context: A multinational corporation operates multiple subsidiaries, each with its own customer relationship management (CRM) system.

      - Use Case: ETL is essential to aggregate and consolidate customer data from various CRM systems into a centralized repository. This enables a holistic view of customer interactions and relationships, fostering better customer service and engagement.

   
   

   4. ELT Scenario - Big Data and Data Lakes:

      - Context: A technology company processes massive amounts of raw sensor data from IoT devices.

      - Use Case: ELT shines in this scenario by loading the raw sensor data directly into a data lake, allowing for flexibility and scalability. Transformations can then be applied within the data lake environment, leveraging the power of big data processing engines for real-time analytics and insights.

   
   

   5. ELT Scenario - Real-Time Data Processing:

      - Context: A financial institution processes streaming data from stock exchanges to make timely investment decisions.

      - Use Case: ELT is suitable for loading raw market data in real-time directly into a data storage system, where transformations are performed on-the-fly. This approach ensures that analysts have immediate access to the latest market information for making time-sensitive investment decisions.

   
   

   6. ELT Scenario - Complex Transformations:

      - Context: A healthcare organization needs to perform complex data transformations on patient records.

      - Use Case: ELT is advantageous in this case, as loading raw patient data into a data lake allows for flexible and scalable processing. Complex transformations, such as anonymization and data enrichment, can be performed within the data lake environment, providing a secure and efficient way to handle sensitive healthcare information.

   These real-life scenarios illustrate the applicability of both ETL and ELT approaches in different business contexts. By understanding the specific requirements and characteristics of each scenario, organizations can leverage Azure Data Factory and Synapse Analytics to implement effective data integration strategies tailored to their unique needs.

Azure security Best Practices

 

As a security best practice, you must disable the Blob public access key and storage account key.

Disable blob public access & Storage account key

Hold on.. This will disable existing shared access signatures. so make sure things are well informed before your perform disable activity.

To secure SAS, please setup Stored access policy as once you setup and share SAS then entire Azure configuration and data are shared with user. 

Best practices for Shared access tokens

So its always advisable to setup Access policy first with permission and then share access tokens.

Once setup, select access policy and setup access token to have control once you share with user.

Talking about Identifity based access - Delegation SAS

 A user delegation SAS is just like a normal SAS, however it is associated with an Azure AD identity, instead of being created with a storage account access key.

Below is a standard architecture where service SAS is accessed using an access key and do not have any link with an active user.

To improve this we can integrate identity-centric security. We can provide SAS access using Azure AD.

For SMB access to Azure Files shares, Azure AD identities can be used for authentication and authorization. This type of access control is for SMB access from domain-joined devices.

To enable this select Active Directory from File share option

select AD "onprem" or "ADDS" whatever suits your need and it will configure identity-based SAS access.