Fabric
- source control:
- Azure DevOps: Mills
- Import On Premise data
- SQL Server: Ball
- Link a Dataverse environment to Microsoft Fabric: Microsoft
- Export Dynamics D365 data to Fabric: Nain (Part I, Part II, Part III, Part IV)
- Data ingestion in Dynamics 365: GitHub
Delta Lake
- real-time analytics
- KQL database: Sharma
- Direct Lake
- semantic model
- fallback behavior: Pawar
- semantic link
- use cases: Pawar
- delta tables
Lakehouse
- sharing
- high concurrency mode
- CI/CD integration (Git + Deployment pipelines)
Notebooks
Data Warehouse (aka Fabric Warehouse)
- security
- granular permissions: Microsoft
- user audit logs: Microsoft
- dynamic data masking: Microsoft
- levels: Strengholt
- Managed Private Endpoints
- from Spark: Murguzur
- restore
- feature
- table cloning
- T-SQL language support
- SP_RENAME
- TRIM
- GENERATE_SERIES
Spark engine
- starter pools
- custom pools
Readiness
- slides: Microsoft
- Dynamics Data in Synapse Link for Dataverse
New Features
- Mirroring Azure SQL Database
- PP: Apr-2024: Microsoft
- Azure Private Link Support for Microsoft Fabric
- PP, Feb-2024: Zhang
- Trusted workspace access: Microsoft
- PP, Feb-2024: Srivastva
- OneLake Shortcuts APIs
- PP, Feb 2024: Hicks
- VNET Data Gateway for Fabric and Power BI
- GA, Feb-2024: Waghani
- Copilot in Fabric
- PP, Jan-2024: Xu
Useful
Books
Sessions
- Integrate your SAP data into Microsoft Fabric [link]
Resources
- Microsoft Fabric Update Blog
- Azure Synapse Analytics Blog
- Fabric community blogs
- Microsoft Fabric Community
- Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric: study guide
- Bradely Ball
- Kevin Chant [GitHub]
- Kevin Conan
- Liliam Cristiman Leme
- Matthew Hicks
- MKowalski [GitHub]
- David Mills
- Aitor Murguzur
- Aman Nain
- Pradeep Pawar [blog]
- Santhosh Kumar Ravindran
- Anshul Sharma
- Meenal Srivastva
- Piethein Strengholt
- Dennes Torres
- Nikki Waghani
- Bert Verbeek [GitHub] on Business Central
- Ruixin Xu
- Dandan Zhang
- Mark Pryce-Maher [YouTube]
Events
DP 600: Implementing Analytics Solutions Using Microsoft Fabric [study guide]
- Maintain a data analytics solution (25–30%)
- Implement security and governance
- Implement workspace-level access controls
- Implement item-level access controls
- Implement row-level, column-level, object-level, and file-level access control
- Apply sensitivity labels to items
- Endorse items
- Maintain the analytics development lifecycle
- Configure version control for a workspace
- Create and manage a Power BI Desktop project (.pbip)
- Create and configure deployment pipelines
- Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
- Deploy and manage semantic models by using the XMLA endpoint
- Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
- Prepare data (45–50%)
- Get data
- Create a data connection
- Discover data by using OneLake data hub and real-time hub
- Ingest or access data as needed
- Choose between a lakehouse, warehouse, or eventhouse
- Implement OneLake integration for eventhouse and semantic models
- Transform data
- Create views, functions, and stored procedures
- Enrich data by adding new columns or tables
- Implement a star schema for a lakehouse or warehouse
- Denormalize data
- Aggregate data
- Merge or join data
- Identify and resolve duplicate data, missing data, or null values
- Convert column data types
- Filter data
- Query and analyze data
- Select, filter, and aggregate data by using the Visual Query Editor
- Select, filter, and aggregate data by using SQL
- Select, filter, and aggregate data by using KQL
- Implement and manage semantic models (25–30%)
- Design and build semantic models
- Choose a storage mode
- Implement a star schema for a semantic model
- Implement relationships, such as bridge tables and many-to-many relationships
- Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
- Implement calculation groups, dynamic format strings, and field parameters
- Identify use cases for and configure large semantic model storage format
- Design and build composite models
- Optimize enterprise-scale semantic models
- Implement performance improvements in queries and report visuals
- Improve DAX performance
- Configure Direct Lake, including default fallback and refresh behavior
- Implement incremental refresh for semantic models
DP 700: Implementing Data Engineering Solutions Using Microsoft Fabric [study guide]
- Implement and manage an analytics solution (30–35%)
- Configure Microsoft Fabric workspace settings
- Configure Spark workspace settings
- Configure domain workspace settings
- Configure OneLake workspace settings
- Configure data workflow workspace settings
- Implement lifecycle management in Fabric
- Configure version control
- Implement database projects
- Create and configure deployment pipelines
- Configure security and governance
- Implement workspace-level access controls
- Implement item-level access controls
- Implement row-level, column-level, object-level, and folder/file-level access controls
- Implement dynamic data masking
- Apply sensitivity labels to items
- Endorse items
- Implement and use workspace logging
- Orchestrate processes
- Choose between a pipeline and a notebook
- Design and implement schedules and event-based triggers
- Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions
- Ingest and transform data (30–35%)
- Design and implement loading patterns
- Design and implement full and incremental data loads
- Prepare data for loading into a dimensional model
- Design and implement a loading pattern for streaming data
- Ingest and transform batch data
- Choose an appropriate data store
- Choose between dataflows, notebooks, KQL, and T-SQL for data transformation
- Create and manage shortcuts to data
- Implement mirroring
- Ingest data by using pipelines
- Transform data by using PySpark, SQL, and KQL
- Denormalize data
- Group and aggregate data
- Handle duplicate, missing, and late-arriving data
- Ingest and transform streaming data
- Choose an appropriate streaming engine
- Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence
- Process data by using eventstreams
- Process data by using Spark structured streaming
- Process data by using KQL
- Create windowing functions
- Monitor and optimize an analytics solution (30–35%)
- Monitor Fabric items
- Monitor data ingestion
- Monitor data transformation
- Monitor semantic model refresh
- Configure alerts
- Identify and resolve errors
- Identify and resolve pipeline errors
- Identify and resolve dataflow errors
- Identify and resolve notebook errors
- Identify and resolve eventhouse errors
- Identify and resolve eventstream errors
- Identify and resolve T-SQL errors
- Optimize performance
- Optimize a lakehouse table
- Optimize a pipeline
- Optimize a data warehouse
- Optimize eventstreams and eventhouses
- Optimize Spark performance
- Optimize query performance
Acronyms:
BI - Business Intelligence
CI/CD - Continuous Integration/Continuous Deployment
DAX -
KQL - Kusto Query Language
GA - General Available
BI - Business Intelligence
CI/CD - Continuous Integration/Continuous Deployment
DAX -
KQL - Kusto Query Language
GA - General Available
PP - Public Preview
PP0 - Private Preview
SQL - Structured Query Language
T-SQL - Transact-SQL
SQL - Structured Query Language
T-SQL - Transact-SQL
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