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I am working on a solution with below components:
- Premium PowerBI Capacity
- Azure Analysis Services (AAS)
- Data Warehouse (star schema ) on Azure SQL
- Azure Data Factory
- This is fully automated solution using azuredevops pipelines.
While, we are already paying for premium capacity, I thought it will be good to migrate the rest to Fabric.
I beleive we will be able to replace AAS with Notebooks but I am not sure about compute costs and other issues which I may face.
Can you please help me to understand more about this migration?
Many thanks in advance.
Solved! Go to Solution.
Hi @Esvani
Welcome to Fabric! Migrating a solution is a complex task. First, you need to identify the primary functions of each component of your current solution, including their inputs, outputs, data structures, and data sizes, .etc. Next, understand the functions of each component in the new Fabric environment and determine which parts of your current solution can be migrated to which components in Fabric. Some components may need to be modified or even completely refactored to be applicable in the new environment. It is also possible that you may ultimately decide to migrate only a part rather than the entire solution.
Based on the current information, it is difficult to provide specific migration advice. You can first review the following documents to understand some of the existing migration methods available. Please note that these methods may only apply to the migration of specific components. If you need to refactor certain components or modify processes, these methods may not be suitable for your solution.
Microsoft Fabric, explained for existing Synapse users | Microsoft Fabric Blog | Microsoft Fabric
Migrating from Azure Synapse Spark to Fabric - Microsoft Fabric | Microsoft Learn
Azure Synapse dedicated SQL pools to Fabric Migration - Microsoft Fabric | Microsoft Learn
Migrate to Data Factory - Microsoft Fabric | Microsoft Learn
In Fabric Data Factory, you can create one or multiple data pipelines in a workspace. These pipelines enable the construction of end-to-end, data-driven workflows for data movement and processing, facilitating the automation of your processes.
Hope this would be helpful.
Best Regards,
Jing
If this post helps, please Accept it as Solution to help other members find it. Appreciate your Kudos!
Hi @Esvani
Welcome to Fabric! Migrating a solution is a complex task. First, you need to identify the primary functions of each component of your current solution, including their inputs, outputs, data structures, and data sizes, .etc. Next, understand the functions of each component in the new Fabric environment and determine which parts of your current solution can be migrated to which components in Fabric. Some components may need to be modified or even completely refactored to be applicable in the new environment. It is also possible that you may ultimately decide to migrate only a part rather than the entire solution.
Based on the current information, it is difficult to provide specific migration advice. You can first review the following documents to understand some of the existing migration methods available. Please note that these methods may only apply to the migration of specific components. If you need to refactor certain components or modify processes, these methods may not be suitable for your solution.
Microsoft Fabric, explained for existing Synapse users | Microsoft Fabric Blog | Microsoft Fabric
Migrating from Azure Synapse Spark to Fabric - Microsoft Fabric | Microsoft Learn
Azure Synapse dedicated SQL pools to Fabric Migration - Microsoft Fabric | Microsoft Learn
Migrate to Data Factory - Microsoft Fabric | Microsoft Learn
In Fabric Data Factory, you can create one or multiple data pipelines in a workspace. These pipelines enable the construction of end-to-end, data-driven workflows for data movement and processing, facilitating the automation of your processes.
Hope this would be helpful.
Best Regards,
Jing
If this post helps, please Accept it as Solution to help other members find it. Appreciate your Kudos!
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