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. 

Transformasi dan Analisis Data dengan Microsoft Fabric

Membuat Lakehouse pertama Anda

Rekayasa Data di Microsoft Fabric

Menjelajahi Data Engineering di Fabric

Opsi

Lakehouse

Warehouse

Data Pipeline

Item penyimpanan dan transformasi data

Lakehouse versus Warehouse

Pengantar Skema Bintang

Skema Bintang

Skema Snowflake

Star schema

Snowflake schema

Skenario Pemodelan Data

Eksplorasi dan Transformasi Data

Agregasi Data

Penggabungan Data

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

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.

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

Kompleksitas kueri rendah karena jumlah join berkurang.

Sering menggunakan lebih banyak ruang penyimpanan karena redundansi data. 

Dicirikan oleh tabel dimensi yang ternormalisasi.

Kompleksitas kueri tinggi karena jumlah join meningkat.

Skenario Pemodelan Data

Transformasi dan Analisis Data dengan Microsoft Fabric

Latihan interaktif praktis