Microsoft Fabric Data Engineering Bootcamp in 6 Weeks by AI First Academy
Master Data Engineering with Real Projects, SQL, Python & Microsoft Fabric – In Just 6 Weeks
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Course overview
The future of data engineering is here — and it’s powered by Microsoft Fabric.
With enterprises shifting to unified data platforms and real-time analytics, professionals with Fabric skills are in high demand.
But most courses only scratch the surface.
We go deeper — and broader.
Our 6-week immersive program doesn’t just teach you Microsoft Fabric.
It transforms you into a job-ready Data Engineer with the skills, portfolio, and confidence to succeed in today’s data-driven world.
✅ What Makes This Course Different (and Better)
🔧 Full-Stack Skill Development
Most Fabric courses skip the fundamentals.
We teach Python + SQL from the ground up so you're never left behind.
Python for Data Engineering: Learn how to transform, clean, and automate data using Pandas and PySpark
SQL for Analysis & Warehousing: Master joins, aggregations, and queries across lakehouses and warehouses
Microsoft Fabric End-to-End: Ingest, process, analyze, and visualize using OneLake, Data Factory, Synapse, Spark, and Power BI
💼 Career-Ready by Design
You won’t just “complete” this course — you’ll come out with:
✔️ A GitHub portfolio of real projects
✔️ A Capstone Project that simulates real-world industry use cases
✔️ A DP-700 Certification Prep Pack (mock exam + study guide)
✔️ A resume optimized for data engineer job roles
✔️ Interview prep to boost your confidence
🎓 Built for Transformation — Not Just Upskilling
Whether you're a data analyst looking to transition, a BI pro leveling up, or starting fresh…
This course is built for career changers and hungry learners, not just seasoned pros.
“Other Fabric bootcamps assume you already know Spark, SQL, and DevOps. We teach it — and help you apply it. That’s what makes this course truly different.”
📚 What You’ll Learn (6-Week Journey)
Week 1 - Fabric + Python + SQL Foundations
Week 2 - Data Ingestion & Medallion Lakehouse
Week 3- PySpark ETL & Data Transformations
Week 4 - Real-Time Data, KQL & Data Warehouse
Week 5 - Power BI Dashboards, Security & CI/CD
Week 6 - Capstone Project + DP-700 Certification Prep
🧪 Every week includes:
- Live Sessions
- Hands-On Labs
- Assignments & Peer Review
- Downloadable Study Guides
Who is this course for
01
Data Analysts ready to move into engineering roles
02
Business Intelligence pros wanting end-to-end data flow mastery
03
Career switchers who want real projects + job support
04
Tech-savvy beginners seeking a guided, structured path
What you’ll get out of this course
Explain the core architecture of Microsoft Fabric, including OneLake, Lakehouse, Data Warehouse, and Power BI integration.
Explain the core architecture of Microsoft Fabric, including OneLake, Lakehouse, Data Warehouse, and Power BI integration.
Differentiate between Microsoft Fabric and other data platforms like Azure Synapse or Databricks, and understand where Fabric fits in
You'll learn how Microsoft Fabric unifies tools like Synapse, Power BI, and Data Factory into one seamless platform—unlike Databricks or standalone services. By Week 1, you'll clearly understand when and why to choose Fabric over other modern data stacks.
Ingest data from diverse sources (e.g., databases, cloud storage, APIs) using Dataflows Gen2 and Pipelines within Fabric.
By Week 2, you'll be ingesting data from APIs, cloud storage, and databases using Dataflows Gen2 and Pipelines in Fabric—automating workflows that mimic real enterprise data ops. Expect hands-on labs that simulate multi-source ingestion and transformation.
Automate ETL/ELT workflows and monitor data pipelines for reliability and performance.
By Week 3, you'll build and automate ETL/ELT workflows using PySpark and Fabric Pipelines. You'll monitor data flow performance, handle failures, and implement validation steps—preparing you to manage production-grade data pipelines with confidence.
Build and manage a Lakehouse using Delta tables and shortcuts for unified data access.
In Week 2 and 3, you’ll design a Lakehouse using Delta tables and implement shortcuts for unified data access across teams. You'll structure data using the Medallion Architecture, enabling scalable, query-ready layers for analytics and reporting.
Use Notebooks (Python, SQL, Spark) within Fabric to perform data transformation, exploration, and basic ML prototyping.
By Week 3, you’ll be hands-on with Fabric Notebooks—using Python, SQL, and PySpark to clean, transform, and explore data. You'll also prototype basic ML workflows, laying the groundwork for AI-ready pipelines inside your Lakehouse.
Create powerful dashboards and reports using Power BI directly within Fabric.
In Week 5, you’ll build interactive dashboards using Power BI directly within Fabric—leveraging Direct Lake access for real-time insights. You'll visualize trends, KPIs, and drill-downs from your Lakehouse and Warehouse data.
Model and transform data with DAX and Power Query to meet business intelligence needs.
Also in Week 5, you'll use DAX and Power Query to shape and model data for business intelligence—creating custom metrics, calculated columns, and filters that drive decision-making in your Power BI reports.
Implement security, access control, and data lineage best practices using Fabric’s built-in governance tools.
By Week 5, you'll implement RBAC, Row-Level Security, and encryption within Fabric. You'll also explore data lineage tools to track data flow and ensure compliance—essential skills for securing and governing enterprise data environments.
Collaborate across teams using workspace roles, versioning, and integration with Microsoft 365.
In Week 5 and 6, you’ll collaborate using Fabric’s workspace roles, version control, and Microsoft 365 integration—sharing notebooks, dashboards, and pipelines securely while enabling real-time teamwork across engineering and analytics teams.
Prepare datasets for AI/ML workflows using Fabric’s Lakehouse and Notebook environments.
By Week 6, you'll prepare structured and semi-structured datasets for AI/ML using Fabric Lakehouse and Notebooks—cleaning, labeling, and transforming data to feed into machine learning pipelines or Azure ML for advanced analytics.
Integrate Fabric with Azure Machine Learning and Copilot experiences to accelerate insights and decision-making.
In the final stretch of the course, you’ll explore how to connect Fabric with Azure Machine Learning and leverage Copilot for code generation, data exploration, and rapid insight delivery—accelerating your path from raw data to intelligent decisions.
What’s included
Live sessions
Learn directly from Gbolade Shada & Davies Bamigboye in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
6 live sessions • 32 lessons • 10 projects
Week 1
Objectives:
Understand what a Data Engineer does
Introduction to Microsoft Fabric architecture (OneLake, Data Factory, Power BI)
Python basics for data work (Pandas, basic syntax, functions)
SQL fundamentals: SELECT, WHERE, GROUP BY, JOIN
Mini-Projects:
Write SQL queries on a Fabric dataset
Python script to clean a CSV and print summary stats
Hands-on: Setting up Fabric workspace and lakehouse, querying sample data
📘 Cert Prep Tie-In: Intro to exam scope (Fabric + Data Fundamentals)
- 📄
Data Engineer Vs Data Analyst vs Data Scientist
- 📄
Overview of Microsoft Fabric & Lakehouse setup
- 📄
Python basics for data work (Pandas, file I/O, functions)
- 📄
SQL fundamentals (SELECT, JOIN, GROUP BY)
- 📄
Cert Prep Tie-In: Intro to exam scope (Fabric + Data Fundamentallesson
- ✍️
Setting up Fabric workspace and lakehouse, querying sample data
Submit by May 7
🟢 Week 1: Foundations – Data Engineering, Microsoft Fabric, SQL & Python Basics
May
3
Week 1 Live Class
Week 2
SQL for data extraction, joins, window functions
Best practices for organizing and querying Lakehouse data
Load and explore sales data in OneLake using SQL
Write optimized queries for KPIs
- 📄
Data Ingestion & Lakehouse Architecture
- 📄
Dataflows Gen2, pipelines, and ingestion sources (Azure, CSV, SaaS)
- 📄
Introduction to Medallion Architecture (Bronze, Silver, Gold)
- ✍️
Hands-on: Ingest and organize data using Medallion design in OneLake
Submit by May 14 - 📄
SQL for data extraction, joins, window functions
- 📄
Best practices for organizing and querying Lakehouse data
- ✍️
Hands on: Load and explore sales data in OneLake using SQL
Submit by May 14 - ✍️
Write optimized queries for KPIs
Submit by May 14 - 📄
Cert Prep Tie-In: Lakehouse concepts, Delta Tables, SQL in Synapse
🟢 Week 2: Data Ingestion & Storage in Fabric (OneLake + SQL Mastery)
Objectives:
• Dataflows Gen2, pipelines, and ingestion sources (Azure, CSV, SaaS)
• Introduction to Medallion Architecture (Bronze, Silver, Gold)
• Hands-on: Ingesting structured/unstructured data into OneLake
Mini-Projects:
📘 Cert Prep Tie-In: Lakehouse concepts, Delta Tables, SQL in Synapse
May
10
Week 2 Live Class
Week 3
Building pipelines using Dataflows Gen2 & Data Factory
Intro to Event Streams for real-time ingestion
Python for data transformation and automation
Using Apache Spark for batch data processing
Transforming data with PySpark and Delta Lake formats
Create a customer feedback pipeline using Data Factory
Transform raw logs into analytics-ready format using Python
- 📄
Python for data transformation and automation
- 📄
Using Apache Spark for batch data processing
- 📄
Transforming data with PySpark and Delta Lake formats
- 📄
Automating ETL workflows using Fabric Notebooks
- 📄
Optional Lab: Data Quality and Validation Techniques
- ✍️
Create a customer feedback pipeline using Data Factory
Submit by May 21 - ✍️
Transform raw logs into analytics-ready format using Python
Submit by May 21 - 📄
Cert Prep Tie-In: Data Factory architecture, ELT vs ETL, Python scrip
🟢 Week 3: ETL Pipelines with Data Factory + Python for Transformation
Objectives:
Mini-Projects:
📘 Cert Prep Tie-In: Data Factory architecture, ELT vs ETL, Python scripting in notebooks
May
17
Week 3 Live Class
Week 4
Spark and PySpark fundamentals (RDD, DataFrame, transformations)
Write PySpark jobs in Fabric notebooks
Batch processing vs streaming
Basic KQL for real-time querying
Fabric Data Warehouse loading, querying, and performance tuning
Hands-on: Build a hybrid ingestion pipeline
Analyze a large clickstream dataset using PySpark
Write Spark job to aggregate data by category and save to Delta Table
- 📄
Real-time ingestion using Event Streams and Eventhouse
- 📄
Basic KQL for real-time querying
- 📄
Fabric Data Warehouse loading, querying, and performance tuning
- ✍️
Hands-on: Build a hybrid ingestion pipeline and monitor performance
Submit by May 28 - 📄
Spark and PySpark fundamentals (RDD, DataFrame, transformations)
- 📄
Write PySpark jobs in Fabric notebooks
- 📄
Batch processing vs streaming
- ✍️
Analyze a large clickstream dataset using PySpark
Submit by May 28 - ✍️
Write Spark job to aggregate data by category and save to Delta Table
Submit by May 28 - 📄
📘 Cert Prep Tie-In: Apache Spark in Fabric, job execution, compute env
🟢 Week 4: Big Data Processing with Apache Spark on Fabric
Objectives:
Mini-Projects:
📘 Cert Prep Tie-In: Apache Spark in Fabric, job execution, compute environments
May
24
Week 4 Live Call
Week 5
Connect Power BI to Fabric (Direct Lake Mode)
Build interactive dashboards with visuals, filters, KPIs
Use Copilot for AI-generated summaries and visuals
Implementing RBAC, RLS, encryption, and access control
Intro to CI/CD: Deploying Fabric pipelines using DevOps tools
Secure data access and build an executive dashboard on product performance
Integrate AI insights using Power BI Copilot
- 📄
Interactive dashboards using Power BI and Direct Lake mode
- 📄
Advanced visualizations and DAX functions
- 📄
Implementing RBAC, RLS, encryption, and access control
- 📄
Intro to CI/CD: Deploying Fabric pipelines using DevOps tools
- ✍️
Secure data access and build a deployable Power BI dashboard
Submit by Apr 18
🟢 Week 5: Data Visualization, Power BI + AI Insights
Objectives:
Mini-Projects:
📘 Cert Prep Tie-In: Power BI in Fabric, AI features, reporting best practices
May
31
Week 5 Live Call
Week 6
Combine OneLake, Data Factory, Spark, Power BI into a full pipeline
Portfolio publishing on GitHub
Prepare for Microsoft Fabric Certification: DP-700 / Data Engineer Associate
Ingest CSVs via Data Factory
Clean & process data using PySpark
Store in OneLake with Delta Table
Visualize in Power BI
DP-700 Exam Guide (Fabric Analytics Engineer Associate)
Mock Exam (Timed Quiz)
Downloadable Study Cheatsheets (Spark, SQL, Fabric)
- 📄
End-to-end project: Ingest → Transform → Store → Visualize
- 📄
DP-700 Certification prep guide and mock exam
- 📄
Portfolio publishing on GitHub
- 📄
Career support: Resume guidance and interview preparation
Jun
7
Week 6 Live Call
🟢 Week 6: Capstone Project + Certification Bootcamp
Objectives:
Capstone Project: 🏁 Full Pipeline:
Cert Prep Add-ons:
The "AI First" Differentiator
Meet your instructor
Gbolade Shada
Gbolade Shada
Certified Fabric Data Engineer & a former IBM data engineer.
Meet Your Instructor – Taiwo Shada
Taiwo Shada is a Certified Microsoft Fabric Data Engineer, software architect, and systems integration expert with more than 10 years of experience building and scaling enterprise data solutions.
During his time at IBM, Taiwo led graduate training programs on Mobile Application Development and Business Intelligence, where he mentored aspiring tech professionals and helped them break into the industry. His ability to simplify complex systems and deliver hands-on instruction has earned him recognition across both technical and academic circles.
Taiwo’s career spans consulting, architecture, and full-stack development — but his specialty lies in data engineering, lakehouse architecture, and systems automation using tools like Microsoft Fabric, Azure, Power BI, and Apache Spark.
What sets Taiwo apart is his practical, real-world teaching style. He doesn’t just teach theory — he builds capstone-ready pipelines, dashboards, and automations that mirror enterprise workflows. His students gain more than knowledge: they leave with confidence, certifications, and portfolio-ready projects.
Whether you’re a beginner aiming to break into data engineering or a BI pro looking to modernize your skillset, Taiwo’s guidance is exactly what you need to master Microsoft Fabric — and launch your next career move.
Davies Bamigboye
Davies Bamigboye
Global tech leader at LSEG; AI & data expert with deep engineering roots
Davies Kenny Bamigboye (DKB) is a seasoned technology leader and AI transformation strategist, with deep roots in data engineering, enterprise infrastructure, and digital innovation.
As a former senior leader at the London Stock Exchange Group (LSEG), Davies led global engineering teams across multiple regions, spearheading critical programs including the implementation of enterprise Identity & Access Management (IAM) platforms and a large-scale data center modernization initiative. These projects were foundational to securing and optimizing the data backbone of one of the world’s largest financial institutions.
Through this work, Davies developed firsthand expertise in the technologies and operational principles that drive complex data ecosystems — the same principles that power Microsoft Fabric today.
Now at the forefront of AI innovation, Davies is the creator of the "AI First" newsletter and the author of AI First: The Leader’s Guide to Building an AI-Centric Organisation.
His dual perspective — spanning enterprise infrastructure and emerging AI tools — brings unmatched clarity and relevance to the world of modern data engineering.
As co-instructor of the Microsoft Fabric Data Engineering Bootcamp, Davies offers a unique blend of big-picture strategy and on-the-ground systems thinking, helping students go beyond tools to master the mindset of real-world engineering leaders.
Course schedule
2h. live class. 4h personal study /week
Wednesdays (Weekly)
11:00AM - 13:00PM
We expect a weekly commitment of 6 hours per week to master the curriculum brojken down thus:
- There will be 2hours live classes weekly.
- Attendees are advised to set aside another 4hours weekly to attend to projects and cours-work.
Weekly projects
4 hours per week
Set aside 4 hours per week to study and work on your assignments.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you