Get an introductory overview of the basic elements of data engineering in Microsoft Fabric, from key solution design items like the star schema to the various features in Fabric supporting data exploration and transformation. 

Transform and Analyze Data with Microsoft Fabric

Creating your first Lakehouse

Data Engineering in Microsoft Fabric

Exploring Fabric Data Engineering

Data storage and transformation items

Lakehouse verus Warehouse

Introduction to Star Schemas

Star Schemas

Snowflake Schemas

Data Modeling Scenarios

Data Exploration and Transformation

Data Aggregation

Data Merging

Data Engineering with Microsoft Fabric

Drill down into specific aspects to consider when implementing star schemas in Microsoft Fabric, including creating fact and dimension tables, managing slowly changing dimensions and other special dimension scenarios, and normalizing and de-normalizing data.


Implementing Star Schemas

Creating Dimension Tables

Fact Table Characteristics

Creating Fact Tables

Slowly Changing Dimensions

SCD Types

Implementing SCD Type 2 Changes

Special Dimension Scenarios

Special Dimensions

Implementing Date Dimensions

Multivalued Dimensions and Bridge Tables

Data Denormalization

Denormalization vs. Normalization

Implementing Data Denormalization

Implement a Star Schema for a Lakehouse or Warehouse

Drill down into key areas of data transformation and exploration: cleansing, filtering, aggregating and merging data.


Data Cleansing and Filtering

Data Cleansing in Action

Converting Data Types

Data Filtering with PySpark

Data Aggregation Scenarios

Implementing Data Aggregation

Merging and Joining Data

Joining using SQL

Merging using PySpark

Explore and Analyze Data

Data Profiling

Use cases for XMLA and SQL Endpoints

Choosing a SQL Query Tool

Transform, Explore, and Analyze Data

Learn about tools available to monitor the performance of a Fabric solution and how to implement performance improvements in the Fabric infrastructure and individual items, including optimization of delta lake tables.

Prestatieproblemen identificeren

Installeer de Fabric Capacity Metrics-app

Gebruik monitoren met de Capacity Metrics-app

Opslag monitoren met de Capacity Metrics-app

Voer prestatieverbeteringen door

Voer prestatieverbeteringen door in Dataflows

Delta Lake-tabeloptimalisatie

Gebruik de OPTIMIZE-bewerking

Gefeliciteerd!

Optimize Performance

Deze cursus behandelt drie belangrijke onderwerpen in Microsoft Fabric: gegevens transformeren, gegevens verkennen en analyseren, en prestaties optimaliseren. Via lessen en interactieve oefeningen leer je verschillende technieken om gegevens te transformeren in Fabric lakehouses en warehouses, van het identificeren van dataproblemen tot het samenvoegen, aggregeren en filteren van gegevens. Je leert ook over verschillende tools en methoden om gegevens in Fabric lakehouses en warehouses te verkennen en te profileren, en hoe je de prestaties van deze Fabric-objecten kunt monitoren en optimaliseren.

Introduction to Microsoft Fabric

Leer hoe je gegevens kunt transformeren en analyseren in je Microsoft Fabric-account.

Gegevens transformeren en analyseren met Microsoft Fabric

Leer hoe je gegevens binnen je Microsoft Fabric-account kunt transformeren en analyseren.

Opslag monitoren met de Capacity Metrics-app

Gegevens transformeren en analyseren met Microsoft Fabric

Praktische interactieve oefening