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. 

Gegevens transformeren en analyseren met Microsoft Fabric

Je eerste Lakehouse maken

Data engineering in Microsoft Fabric

Fabric Data Engineering verkennen

Opties

Lakehouse

Warehouse

Data Pipeline

Gegevensopslag en transformatie-items

Lakehouse versus Warehouse

Introductie tot sterren­schema’s

Stermodellering

Snowflake-schema's

Star schema

Snowflake schema

Datamodelleringsscenario's

Gegevens verkennen en transformeren

Gegevensaggregatie

Gegevens samenvoegen

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

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.

Identifying performance issues

Install the Fabric Capacity Metrics app

Monitoring Usage with the Capacity Metrics App

Monitoring Storage with the Capacity Metrics App

Implement performance improvements

Implement performance improvements in Dataflows

Delta Lake table optimization

Use the OPTIMIZE Operation

Congratulations!

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.

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

Lage querycomplexiteit door minder joins.

Gebruikt vaak meer opslagruimte door gegevensredundantie. 

Wordt gekenmerkt door genormaliseerde dimensionale tabellen.

Hoge querycomplexiteit door een groter aantal joins.

Datamodelleringsscenario's

Gegevens transformeren en analyseren met Microsoft Fabric

Praktische interactieve oefening