Highlights
Fabric Training:
- Introduction to Microsoft Fabric
- Training on Microsoft Fabric Foundation
- Data Engineering - OneLake & LakeHouse plus ETL
- Explore Data Factory Introduction & End-to-End build
- Hands-on experience in data movement and data science
- Real-time analytics applications covered
- Practical exercises in data engineering
- Overview of Data Science & Data Management
- Seamless integration of diverse data sources
With hands-on exercises and expert guidance, you'll gain proficiency in managing data lakes, executing data engineering tasks, and seamlessly integrating data sources. Elevate your skills and empower your career with our Fabric training course.
Course Details
Introduction to Fabric
- Introduction and Learning objectives
Microsoft Fabric Foundation
- Overview of Microsoft Fabric
- Lakehouse vs Warehouse
- Fabric License Types
- Getting-Started
- Concept of Workspaces
- Create & Configure Workspace Access
- Workspace Settings
Data Engineering - OneLake
- Introduction to Data Engineering in Fabric.
- Introduction to OneLake
- Lakehouse
- Delta Lake
- OneLake Explorer
- Authentication and Authorization
- Monitoring Hub and Data Hub
Data Engineering - Lakehouse
- Introduction
- Architecture
- Distinctions between Lakehouse & Warehouse
Data Engineering - ETL with Lakehouse
- What is Spark?
- Notebook Overview
- Web based and VS Code Notebooks
- Spark + Monitoring Spark Jobs
Data Warehouse - Serverless Engine
- Warehouse-SQL Scripts
- Default Dataset and Modelling
- Ingest Methods
- Load Data Introduction
- Load Data into Lakehouse
- Load Data Using Data Pipeline Part 1
- Load Data Using Dataflows
- Load Data Using Data Pipeline Part 2
- Models and Power BI reports
- Cross-database Queries
- Roles + Permissions (RLS, CLS)
- Manage Performance
- Warehouse-SQL Scripts
Overview of Real-Time Analytics
- Real-Time Analytics - KQL Scripts
- SQL vs KQL Introduction
- Create, Process and Monitor
- KSQL Queryset
- KSQL Database
- KSQLmagic
- Spark
- Real-Time Analytics - KQL Scripts
Data Factory Introduction
- What is Data Factory?
- Data Flows and Pipelines
- Architecture
- Workspace Setup
Data Factory End-to-End Build
- Control Table and Copy Data
- Metadata Copy Pattern
- Script Activity
- Data Flows Gen2
- Execute Pipeline
- Shortcut to Other Workspaces
- Notebooks
- Data Flow Gen2 Transformations
- Pipelines, Notebooks and Parameters
- Monitoring Notebooks in Pipelines
Overview of Data Visualization with Power BI
- Power BI and Fabric
- Version Control
- Direct Lake
Overview of Data Science
- Data Science - Resources
- What is Data Science?
- The Data Science Process
- Items and Models
- Excercise
- Model Management
- Data Science - Resources
Overview of Data Management
- Introduction
- Access Control
- Governance
- Monitoring
Who should attend
- Data analysts, Data engineers, Data Architects, Business intelligence professionals with some experience in Power BI, Python, Spark
- Companies that are using Power BI
- Data scientists
- IT professionals working with enterprise analytics
- Professionals seeking to enhance their skills in data management and analytics
- All Delegates should have experience in Power BI and ideally some understanding of Python, Spark
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some topics that we needed to know. The instructor genuinely cared about our learning. We felt supported from start to finish and left with knowledge that truly mattered to our work" Brian Leek, Data Analyst, May 2022
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices - very impressive” Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022