🏭🗒️Microsoft Fabric: Data Mesh [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 23-May-2024

[Microsoft Fabric] Data Mesh

• {definition} a type of decentralized data architecture that organizes data based on different business domains [2]
•  ⇐ a centrally managed network of decentralized data products
• {concept} landing zone
• typically a subscription that needs to be governed by a common policy [7]
• {downside} creating one landing zone for every project can lead to too many landing zones to manage
• {alternative} landing zones based on a business domain [7] 
•  resources must be managed efficiently in a way that each team is given access to only their resources [7]
• ⇐  shared resources might be need with separate management and common access to all [7]
• need to be linked together into a mesh
• via peer-to-peer networks
• {concept} connectivity hub
• {feature} resource group
• {definition} a container that holds related resources for an Azure solution 
• can be associated with a data product
• when the data product becomes obsolete, the resource group can be deleted [7]
• {feature} subscription
• {definition} a logical unit of Azure services that are linked to an Azure account
• can be associated as a landing zone governed by a policy [7]
• {feature} tenant (aka Microsoft Fabric tenant, MF tenant)
• a single instance of Fabric for an organization that is aligned with a Microsoft Entra ID
• can contain any number of workspaces
• {feature} workspaces
• {definition} a collection of items that brings together different functionality in a single environment designed for collaboration
• associated with a domain [3]
• {feature} domains
• {definition} a way of logically grouping together data in an organization that is relevant to a particular area or field [1]
• some tenant-level settings for managing and governing data can be delegated to the domain level [2]
• {feature} subdomains
  • a way for fine tuning the logical grouping data under a domain [1]
  • subdivisions of a domain
• {concept} deployment template
• 
  

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References

[1] Microsoft Learn: Fabric (2023) Fabric domains (link)

[2] Establishing Data Mesh architectural pattern with Domains and OneLake on Microsoft Fabric, by Maheswaran Arunachalam (link) 

[3] Data mesh: A perspective on using Azure Synapse Analytics to build data products, by Amanjeet Singh (link)

[4] Zhamak Dehghani (2021) Data Mesh: Delivering Data-Driven Value at Scale

[5] Marthe Mengen (2024) How do you set up your Data Mesh in Microsoft Fabric? (link)

[6] Administering Microsoft Fabric - Considering Data Products vs Domains vs Workspaces, by Paul Andrew (link)

[7] Aniruddha Deswandikar (2024) Engineering Data Mesh in Azure Cloud

Resources:

[R1] Microsoft Learn (2025) Fabric: What's new in Microsoft Fabric? [link]

🏭🗒️Microsoft Fabric: Warehouse [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 11-Mar-2024

Warehouse vs SQL analytics endpoint in Microsoft Fabric [3]

[Microsoft Fabric] Warehouse

• {def} highly available relational data warehouse that can be used to store and query data in the Lakehouse
• supports the full transactional T-SQL capabilities 
• modernized version of the traditional data warehouse
• unifies capabilities from Synapse Dedicated and Serverless SQL Pools
• resources are managed elastically to provide the best possible performance
• ⇒ no need to think about indexing or distribution
• a new parser gives enhanced CSV file ingestion time
• metadata is cached in addition to data
• improved assignment of compute resources to milliseconds
• multi-TB result sets are streamed to the client
• leverages a distributed query processing engine
• provides with workloads that have a natural isolation boundary [3]
• true isolation is achieved by separating workloads with different characteristics, ensuring that ETL jobs never interfere with their ad hoc analytics and reporting workloads [3]
• {operation} data ingestion
• involves moving data from source systems into the data warehouse [2]
• the data becomes available for analysis [1]
• via Pipelines, Dataflows, cross-database querying, COPY INTO command
• no need to copy data from the lakehouse to the data warehouse [1]
• one can query data in the lakehouse directly from the data warehouse using cross-database querying [1]
• {operation} data storage
• involves storing the data in a format that is optimized for analytics [2]
• {operation} data processing
• involves transforming the data into a format that is ready for consumption by analytical tools [1]
• {operation} data analysis and delivery
• involves analyzing the data to gain insights and delivering those insights to the business [1]
• {operation} designing a warehouse (aka warehouse design)
• standard warehouse design can be used
• {operation} sharing a warehouse (aka warehouse sharing)
• a way to provide users read access to the warehouse for downstream consumption
• via SQL, Spark, or Power BI
• the level of permissions can be customized to provide the appropriate level of access
• {feature} mirroring 
• provides a modern way of accessing and ingesting data continuously and seamlessly from any database or data warehouse into the Data Warehousing experience in Fabric
• any database can be accessed and managed centrally from within Fabric without having to switch database clients
• data is replicated in a reliable way in real-time and lands as Delta tables for consumption in any Fabric workload
• {feature} v-order
• write time optimization to the parquet file format that enables lightning-fast reads under the MF compute engine [5]
• {feature} caching
• stores frequently accessed data and metadata in a faster storage layer [6]
• {feature} snapshots
• {def} read-only representation of a warehouse at a specific point in time [7]
• {feature} automatic purging
• routinely and systematically eliminating expired data periodically [8] 
• {benefit} proactively helps maintain an efficient and cost-effective data infrastructure [8]
• via garbage collection
• background process, that periodically identifies and cleans [8] 
• all the data and log files of dropped tables
• aborted transactions
• temporary tables
• expired files
• executes every 24 hours, when the warehouse is active [8] 
• ensures the data warehouse remains optimized and efficient [8]
• {goal} storage cost optimization
• {goal} minimize maintenance overhead
• {goal} adhering to data retention regulations

• {concept} SQL analytics endpoint 
• a warehouse that is automatically generated from a Lakehouse in Microsoft Fabric [3]
• {concept} virtual warehouse
• can containing data from virtually any source by using shortcuts [3]
• {concept} cross database querying 
• enables to quickly and seamlessly leverage multiple data sources for fast insights and with zero data duplication [3]

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References:

[1] Microsoft Learn (2023) Fabric: Get started with data warehouses in Microsoft Fabric (link) 

[2] Microsoft Learn (2023) Fabric: Microsoft Fabric decision guide: choose a data store (link)
[3] Microsoft Learn (2024) Fabric: What is data warehousing in Microsoft Fabric? (link)

[4] Microsoft Learn (2023) Fabric: Better together: the lakehouse and warehouse (link)

[5] Microsoft Learn (2024) Fabric: Understand V-Order for Microsoft Fabric Warehouse [link]

[6] Microsoft Learn (2024) Fabric: Caching in Fabric data warehousing [link]

[7] Microsoft Learn (2024) Fabric: Warehouse Snapshots in Microsoft Fabric (Preview) [link]

[8] Microsoft Fabric Updates Blog (2025) Intelligent Data Cleanup: Smart Purging for Smarter Data Warehouses [link] 

Resources:
[R1] Microsoft Learn (2023) Fabric: Data warehousing documentation in Microsoft Fabric (link)

[R2] Microsoft Learn (2025) Fabric: What's new in Microsoft Fabric? [link]
[R3] Microsoft Learn (2025) Fabric: Share your data and manage permissions [link]
[R4] Microsoft Learn (2025) Microsoft Fabric decision guide: Choose between Warehouse and Lakehouse [link]

Acronyms:

ETL - Extract, Transfer, Load

MF - Microsoft Fabric