Lab: Prepare the data using Dataflow Gen2 and data pipeline - Microsoft Fabric Tutorial

Contents

• Introduction Introduction

  • Introduction to data analysis and Microsoft Fabric

    4m 38s
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    Introduction to Microsoft Fabric components

    14m 41s
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    Lakehouses, data warehouses, notebooks, dataflows, and more

    2m 24s
  • (Locked)
    Fabric admin tasks, admin center, and managing user access

    5m 59s
  • (Locked)
    Lab: Enable a Microsoft Fabric license

    12m
• 1. Data Warehousing and SQL Fundamentals 1. Data Warehousing and SQL Fundamentals

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    Understand relational databases and SQL fundamentals

    8m 47s
  • Lab: SQL commands to understand relational databases

    25m 17s
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    Design and implement data warehouses in Microsoft Fabric

    14m 38s
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    ETL data into the Microsoft Fabric data warehouse

    7m 14s
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    Load data using data pipeline, Dataflow Gen2, and T-SQL

    7m 21s
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    Monitoring and managing data warehouses

    6m 50s
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    Lab: Create a data warehouse and load and ingest data into it

    16m 23s
• 2. Data Ingestion and Preparation 2. Data Ingestion and Preparation

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    Data ingestion using Dataflow Gen2 and data pipeline

    7m 26s
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    Create and manage data pipelines for efficient data movement

    5m 54s
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    Use Dataflow Gen2 for data preparation and transformation

    2m 43s
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    Best practices for data ingestion and preparation

    4m 21s
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    Lab: Prepare the data using Dataflow Gen2 and data pipeline

    34m 26s
• 3. Lakehouses, Semi-Structured Data, and PySpark 3. Lakehouses, Semi-Structured Data, and PySpark

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    Introduction to lakehouse architecture in Microsoft Fabric

    7m 3s
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    Store and manage semi-structured data in lakehouses

    1m 26s
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    Work with Delta Lake tables for efficient data management

    2m 28s
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    Use PySpark for data transformation and analysis

    3m 31s
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    Lab: Prepare the data using PySpark

    17m 15s
• 4. Medallion Architecture in Microsoft Fabric 4. Medallion Architecture in Microsoft Fabric

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    Understand the concept of medallion architecture

    5m 2s
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    Design and implement medallion architecture for data management

    4m 21s
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    Organize data lakes, data warehouses, and lakehouses

    5m 55s
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    Best practices for scalable and efficient data architectures

    5m 18s
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    Lab: Get hands-on to better understand medallion architecture

    27m 2s
• 5. Exploring and Analyzing Data 5. Exploring and Analyzing Data

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    Explore and analyze data in Microsoft Fabric

    6m 14s
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    Use Power BI for data exploration and visualization

    2m 16s
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    Perform descriptive and diagnostic analysis on datasets

    5m 1s
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    Extract insights and make data-driven decisions

    4m 45s
• 6. Power BI and Data Visualization 6. Power BI and Data Visualization

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    Introduction to Power BI and its role in data visualization

    4m 56s
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    Create interactive reports and dashboards

    3m 58s
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    Use Power BI for data exploration and analysis

    7m 3s
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    Best practices for effective data visualization

    7m 43s
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    Lab: Create interactive reports and publish to Power BI service

    1h 19m 55s
• 7. Designing and Building Semantic Models 7. Designing and Building Semantic Models

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    The importance of semantic modeling in data analytics

    9m 13s
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    Design tabular models for semantic analysis in Power BI

    4m 48s
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    Implement relationships and calculations using DAX

    5m 25s
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    Optimize semantic models for performance and scalability

    1m 52s
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    Implement security settings in Microsoft Fabric

    10m 6s
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    Lab: Manage sensitivity labels in semantic models and lakehouses

    9m 56s
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    Lab: DAX Studio and Tabular Editor 2

    21m 44s
• Conclusion Conclusion

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    Summary

    3m 29s