CMS/DP-700 : Microsoft Fabric Data Engineer

4 gün (24 Saat) Orta Sınıf / Online İş Zekası ve İleri Analitik

Bu eğitim, Microsoft Fabric platformunu uçtan uca anlamak ve etkin bir şekilde kullanmak isteyen veri profesyonelleri için hazırlanmıştır. Katılımcılar, lakehouse mimarilerinden veri ambarlarına, gerçek zamanlı analitikten Delta Lake tablolarına kadar Fabric’in sunduğu tüm bileşenleri uygulamalı olarak deneyimleyeceklerdir. Eğitim boyunca, veri yükleme desenleri, veri mimarileri ve orkestrasyon süreçlerinin nasıl tasarlanıp uygulanacağı detaylı bir şekilde ele alınacaktır. Program, yalnızca teorik bilgi sunmakla kalmaz; Spark, KQL, SQL ve PySpark gibi teknolojiler üzerinden pratik alıştırmalarla yetkinlik kazandırmayı hedefler. Katılımcılar, veri güvenliği, izleme, CI/CD entegrasyonu ve Copilot desteği gibi ileri seviye konulara da hakim olarak modern veri platformlarında uçtan uca çözümler geliştirme becerisi kazanırlar. Eğitim sonunda katılımcılar, Microsoft Fabric üzerinde ölçeklenebilir, güvenli ve yönetilebilir veri çözümlerini hayata geçirebilecek bilgi birikimine sahip olacaklardır.

Eğitim İçeriği

Module 1: Explore end-to-end analytics with Microsoft Fabric

  • What is Microsoft Fabric
  • Data teams and Fabric
  • Enable and use Microsoft Fabric

Module 2: Get started with lakehouses in Microsoft Fabric

  • What is a lakehouse?
  • Work with a Fabric lakehouse
  • Load data into a lakehouse
  • Explore, transform, and visualize data in the lakehouse

Module 3: Use Apache Spark in Microsoft Fabric

  • What is Apache Spark?
  • How to use Apache Spark?
  • Prepare to use Apache Spark
  • Configure the Spark environment
  • Run Spark in Fabric
  • Ingest data with Spark
  • Load data in a Spark Dataframe
  • Transform data in a Spark Dataframe
  • Work with data using Spark SQL
  • Query data using the Spark SQL API
  • Visualize data

Module 4: Work with Delta Lake tables in Microsoft Fabric

  • Understand Delta Lake
  • Create Delta tables using code in Spark
  • Managed vs external tables
  • Work with Delta tables in Spark
  • Spark Structured Streaming
  • Use Delta lake as a streaming source
  • Use Delta lake as a streaming sink
  • OptimizeWrite function
  • Optimize command
  • V-Order function
  • Vacuum command
  • Partition files
  • Data versioning and time travel

Module 5: Ingest data with Dataflows (Gen2) in Microsoft Fabric

  • Understand Dataflow Gen2
  • Dataflow Gen2 benefits and limitations
  • Explore Dataflow Gen2
  • Integrate Dataflow Gen2 and pipelines

Module 6: Orchestrate processes and data movement

  • Pipelines in Microsoft Fabric
  • Common Activities – copy data
  • Use templates for common activities
  • Run and monitor pipelines

Module 7: Organize a Fabric lakehouse using medallion architecture design

  • Medallion architecture overview
  • Move and transform data across layers
  • Implement a medallion architecture
  • Query and report on data in your Fabric lakehouse
  • Secure the medallion layers

Module 8: Get started with Real-Time Intelligence in Microsoft Fabric

  • What is real-time analytics?
  • Real-Time Intelligence in Microsoft Fabric
  • Real-Time Hub
  • Ingest and transform real-time data
  • Store and query real-time data
  • Visualize real-time data
  • Automate actions

Module 9: Use real time eventstreams in Microsoft Fabric

  • Components of eventstreams
  • Eventstream sources and destinations
  • Eventstream transformations

Module 10: Work with real-time data in a Microsoft Fabric eventhouse

  • Get started with an eventhouse
  • Use KQL effectively
  • Materialized views and stored functions

Module 11: Get started with data warehouses in Microsoft Fabric

  • Data warehouse fundamentals
  • Understand Fabric warehouses
  • Create a data warehouse in Fabric
  • Design a data warehouse
  • Special types of dimension tables
  • Data warehouse schema design
  • Ingest data into a data warehouse
  • Clone tables
  • Query data
  • Visualize queries
  • Build relationships
  • Understand the default semantic model
  • Visualize data
  • Security overview
  • Workspace and item permissions

Module 12: Load data into a Microsoft Fabric data warehouse

  • Understand ETL (Extract, Transform and Load)
  • Stage the data
  • Different data load types
  • Dimension keys
  • Create tables with T-SQL
  • Load dimension tables
  • Load fact tables
  • Load data with Fabric pipelines
  • Configure the copy data assistant
  • Load data with T-SQL
  • Load from other assets
  • Load data with Dataflows Gen2
  • Transform data with Copilot

Module 13: Monitor a Microsoft Fabric data warehouse

  • Monitor capacity metrics
  • Warehouse operation categories
  • Timepoint explore graph
  • Usage considerations
  • Monitor current activity
  • Query insights views

Module 14: Secure a Microsoft Fabric data warehouse

  • Dynamic Data Masking
  • Masking Rules
  • How to configure Dynamic Data Masking
  • Row-level security
  • How to implement row-level security
  • Row-level security example
  • Column-level security
  • Granular permissions using T-SQL

Module 15: Implement continuous integration/continuous delivery (CI/CD)

  • Understand (CI/CD)
  • Best practices when working with multiple developers
  • Use CI/CD in Fabric
  • Implement version control and Git integration
  • Commit and sync changes
  • Branching scenarios
  • Implement deployment pipelines
  • Use deployment pipelines with Git
  • Use the Fabric REST APIs

Module 16: Monitor activities in Microsoft Fabric

  • Monitor Fabric activities
  • Monitoring best practices
  • Use the Monitoring Hub
  • Take action with Activator
  • Use cases for Activator

Module 17: Secure data access in Microsoft Fabric

  • Secure data by workload and job responsibilities
  • Understand the Fabric security model
  • Data security design
  • Understand workspace roles
  • Configure workspace and item-level permissions
  • Configure lakehouse read access
  • Use T-SQL to configure granular permissions
  • Use OneLake data access roles to secure data

Module 18: Administer Microsoft Fabric

  • Understand the Fabric Architecture
  • Understand Fabric concepts
  • Fabric admin tasks
  • Fabric admin tools
  • Fabric admin portal
  • Fabric admin monitoring workspace
  • Fabric capacity metrics app
  • Manage Fabric security
  • Assign and manage user licenses
  • Govern data in Fabric

Labs

  • Create a Microsoft Fabric Lakehouse
  • Analyze data with Apache Spark
  • Use delta tables in Apache Spark
  • Create a medallion architecture in a Microsoft Fabric lakehouse
  • Ingest data with a pipeline in Microsoft Fabric
  • Create and use Dataflows (Gen2) in Microsoft Fabric
  • Analyze data in a data warehouse
  • Load data into a warehouse using T-SQL
  • Query a data warehouse in Microsoft Fabric
  • Monitor a data warehouse in Microsoft Fabric
  • Secure a Microsoft Fabric data warehouse
  • Get started with Real-Time Intelligence in Microsoft Fabric
  • Get started with data science in Microsoft Fabric
  • Explore data for data science with notebooks in Microsoft Fabric
  • Preprocess data with Data Wrangler in Microsoft Fabric
  • Train and track machine learning models with MLflow in Microsoft Fabric
  • Generate batch predictions using a deployed model in Microsoft Fabric
  • Ingest real-time data with Eventstream in Microsoft Fabric
  • Use Activator in Microsoft Fabric
  • Work with data in a Microsoft Fabric eventhouse
  • Get started with Real-time Dashboards in Microsoft Fabric
  • Create DAX calculations in Power BI Desktop
  • Design scalable semantic models
  • Create reusable Power BI assets
  • Enforce semantic model security
  • Monitor Fabric activity in the monitoring hub
  • Secure data access in Microsoft Fabric
  • Work with SQL Database in Microsoft Fabric
  • Work with API for GraphQL in Microsoft Fabric
  • Implement deployment pipelines in Microsoft Fabric
  • Work smarter with Copilot in Microsoft Fabric Dataflow Gen2
  • Analyze data with Apache Spark and Copilot in Microsoft Fabric notebooks
  • Use Copilot in Microsoft Fabric data warehouse
  • Chat with your data using Microsoft Fabric data agents

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