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.

Mengidentifikasi masalah kinerja

Instal aplikasi Fabric Capacity Metrics

Memantau Penggunaan dengan Aplikasi Capacity Metrics

Memantau Penyimpanan dengan Capacity Metrics App

Menerapkan peningkatan kinerja

Menerapkan peningkatan kinerja di Dataflows

Optimisasi tabel Delta Lake

Gunakan Operasi OPTIMIZE

Selamat!

Optimize Performance

Kursus ini berfokus pada tiga topik utama Microsoft Fabric: mentransformasi data, mengeksplorasi dan menganalisis data, serta mengoptimalkan kinerja. Melalui pelajaran dan latihan interaktif, Anda akan mempelajari berbagai teknik untuk mentransformasi data di lakehouse dan warehouse Fabric, mulai dari mengidentifikasi masalah pada data hingga menggabungkan, mengagregasi, dan memfilter data. Anda juga akan mempelajari berbagai alat dan metode untuk mengeksplorasi dan memprofilkan data di lakehouse dan warehouse Fabric, serta cara memantau dan mengoptimalkan kinerja objek Fabric tersebut.

Introduction to Microsoft Fabric

Pelajari cara mengubah dan menganalisis data dalam akun Microsoft Fabric Anda.

Transformasi dan Analisis Data dengan Microsoft Fabric

Pelajari cara mentransformasi dan menganalisis data di akun Microsoft Fabric Anda.

Memantau Penyimpanan dengan Capacity Metrics App

Transformasi dan Analisis Data dengan Microsoft Fabric

Latihan interaktif praktis