🏭🗒️Microsoft Fabric: Data 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] Data Warehouse

• 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
• modernized with key improvements
• 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 now 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
• {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: Fabric (2023) Get started with data warehouses in Microsoft Fabric (link) 
[2] Microsoft Learn: Fabric (2023) Microsoft Fabric decision guide: choose a data store (link)
[3] Microsoft Learn: Fabric (2024) What is data warehousing in Microsoft Fabric? (link)
[4] Microsoft Learn: Fabric (2023) Better together: the lakehouse and warehouse (link)

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