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Hi,
I'd like to know which possible architectures it is possible to implement with Fabric, apart from the medaillon architecture.
Thanks
Solved! Go to Solution.
Hi @pmscorca ,
Thank you for reaching out to us on the Microsoft Fabric Community Forum.
The Lambda Architecture integrates both batch and real-time data processing, and Microsoft Fabric facilitates this through pipelines, notebooks, warehouses, Real-Time Analytics (KQL), and event streams. A practical example of this approach is the implementation of a greenfield lakehouse in Microsoft Fabric, as outlined in the Azure Architecture Center. This resource includes an overview and an architecture diagram that illustrates the data flow and key components.
This example showcases a greenfield approach to building a scalable data platform using Microsoft Fabric and the lakehouse design paradigm. Fabric seamlessly integrates data storage, processing, and analytics, enabling the creation of a future-proof and efficient data ecosystem from the ground up.
You may find the following documentation helpful. Please take a look:
Lakehouse end-to-end scenario: overview and architecture - Microsoft Fabric | Microsoft Learn
Greenfield lakehouse on Microsoft Fabric - Azure Architecture Center | Microsoft Learn
Thank you and Regards,
Menaka
Fabric is a rather versatile set of tools so there are many different ways you could structure your data architecture.
A traditional data warehouse (Kimbal) set up is easily achievable using the warehouse with star schema and sql endpoints.
Data mesh for a more decentralized architecture. Use the Lakehouse and domains to establish ownership and governance and implement one lake for distributed storage. Then direct lake mode in PBI for domain-oriented consumption.
You could do a Lamda architecture; for your batch layer you have pipelines, notebooks and warehouses. For your speed layer you have real-time analytics (KQL) and event streams. Last, PBI direct lake, KQL queries for your serving layer.
Please mark this post as solution if it helps you. Appreciate Kudos.
Ok, but it could be useful to have the reference schema or diagram for each possible architecture.
Hi @pmscorca ,
Thank you for reaching out to us on the Microsoft Fabric Community Forum.
The Lambda Architecture integrates both batch and real-time data processing, and Microsoft Fabric facilitates this through pipelines, notebooks, warehouses, Real-Time Analytics (KQL), and event streams. A practical example of this approach is the implementation of a greenfield lakehouse in Microsoft Fabric, as outlined in the Azure Architecture Center. This resource includes an overview and an architecture diagram that illustrates the data flow and key components.
This example showcases a greenfield approach to building a scalable data platform using Microsoft Fabric and the lakehouse design paradigm. Fabric seamlessly integrates data storage, processing, and analytics, enabling the creation of a future-proof and efficient data ecosystem from the ground up.
You may find the following documentation helpful. Please take a look:
Lakehouse end-to-end scenario: overview and architecture - Microsoft Fabric | Microsoft Learn
Greenfield lakehouse on Microsoft Fabric - Azure Architecture Center | Microsoft Learn
Thank you and Regards,
Menaka
Hi @pmscorca ,
May I ask if you have resolved this issue? If so, please mark the helpful reply and accept it as the solution. This will be helpful for other community members who have similar problems to solve it faster.
Thank you.
Hi @pmscorca ,
I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If my response has addressed your query, please accept it as a solution and give a 'Kudos' so other members can easily find it.
Thank you.
Hi @pmscorca ,
I hope this information is helpful. Please let me know if you have any further questions or if you'd like to discuss this further. If this answers your question, please Accept it as a solution and give it a 'Kudos' so others can find it easily.
Thank you.
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