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#1 Navigating Fabric Ecosystem
1️⃣WHY Microsoft Fabric?
2️⃣WHAT is MS Fabric?
3️⃣Tenant -> Capacity -> Domains -> Workspaces -> Items
4️⃣Core Fabric components (high-level overview)
5️⃣Pricing model
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#2 OneLake
1️⃣Overview of data architectures
2️⃣Overview of OneLake - concepts and technical implementation
3️⃣Inside Delta Parquet storage format
4️⃣All roads lead to OneLake
5️⃣Introducing Shortcuts and Mirroring
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#3 Ingest or Access Data
1️⃣Data Factory as a workload
2️⃣Dataflows Gen2
3️⃣Pipeline
4️⃣Copy data activity
5️⃣Copy job
6️⃣Data loading methods: use cases and common scenarios
7️⃣Apache Airflow
8️⃣Scheduling and triggers
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#4 Data Engineering
1️⃣Fundamentals of data engineering
2️⃣Fundamentals of a lakehouse
3️⃣Understanding the Spark engine fundamentals
4️⃣Medallion design pattern fundamentals
5️⃣Notebooks
6️⃣Querying and working with the data in the lakehouse
7️⃣Limitations and considerations
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#5 Data Warehousing
1️⃣Fundamentals of data warehousing
2️⃣Warehouse vs Lakehouse: which one is for me?
3️⃣Warehouse core elements: schemas, tables, views, sprocs
4️⃣Advanced features (clone table, time travel, etc.)
5️⃣Query and work with the warehouse data
6️⃣Limitations and considerations
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#6 Direct Lake for Power BI
1️⃣What is Direct Lake?
2️⃣Direct Lake inner workings
3️⃣Two types of Direct Lake
4️⃣Core concepts in Direct Lake (syncing, framing, transcoding, temperature)
5️⃣Analyze query processing
6️⃣Creating semantic models and reports with Direct Lake
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#7 Real-Time Intelligence
1️⃣Stream processing vs. batch processing
2️⃣Real-time hub
3️⃣Eventhouse
4️⃣KQL database
5️⃣KQL queryset
6️⃣Real-time dashboard
7️⃣Activator
8️⃣KQL fundamentals (data transformation, data processing, etc.)
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#8 Administration, Monitoring, Security, CI/CD
1️⃣Administration: Admin portal and various settings
2️⃣Monitoring: different "levels" of monitoring
3️⃣Security: current security model + OneLake security
4️⃣CI/CD options: Workspace Git integration, Deployment pipelines
5️⃣Best practices for lifecycle management
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#9 AI Features
1️⃣Copilots
2️⃣Data Agents
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#10 Prepare Data for Fabric Workloads
1️⃣Choose between the lakehouse, warehouse, or eventhouse
2️⃣OneLake integration for semantic models and eventhouse
3️⃣Create views, functions, and stored procedures
4️⃣Enrich data by adding new columns or tables
5️⃣Implement a star schema for the lakehouse or warehouse
6️⃣Denormalize, aggregate, and merge data
7️⃣Identify and resolve duplicates and missing values
8️⃣Change column data types
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#11 Advanced Concepts and Techniques
1️⃣Incremental refresh with pipelines, notebooks, and Dataflows
2️⃣Metadata-driven pipelines
3️⃣Implementing a medallion design pattern in Microsoft Fabric
4️⃣Semantic link
5️⃣Configure Spark settings
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#12 Optimizing Fabric Workloads
1️⃣Optimizing lakehouse tables: OPTIMIZE, VACUUM, V-order
2️⃣Optimize SQL queries for a warehouse
3️⃣Optimize the Direct Lake model performance
4️⃣Optimize Power BI semantic models
