Data Engineering · Beginner

Azure Data Engineering + Microsoft Fabric + AI

Job-ready Azure data engineering with SQL, Python, ADF, Databricks, Synapse, Microsoft Fabric and AI tools.

  • SQL
  • Python
  • PySpark
  • Azure Data Factory
  • Databricks
  • Synapse
  • Microsoft Fabric
  • AI tools
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About this course

Build strong fundamentals, practical skills and interview readiness as an Azure Data Engineer, using Microsoft Fabric and AI tools, with minimal coding complexity.

Who this is for

  • IT professionals with 3–7 years of experience: developers, DBAs, BI analysts and ETL engineers
  • Career switchers from non-IT backgrounds
  • Informatica or SSIS professionals moving to the cloud
  • Data analysts using SQL and Power BI
  • Freshers and graduates
  • Azure beginners with no cloud background

What you will be able to do

  • Strong SQL, Python, data warehousing and cloud fundamentals
  • Hands-on practice with ADF, Databricks, Synapse and Microsoft Fabric
  • End-to-end data pipelines for your portfolio
  • Interview practice, an ATS-format resume and an optimized LinkedIn profile

Prerequisites: No prerequisites. Suitable for freshers and Azure beginners.

8-week timeline and 10 modules

11
Learning units
54
Topics covered
2
Projects
12
Tools

  1. Weeks 1–2 · Module 1

    SQL Server and SQL

    • SQL Server setup and real-time scenarios
    • DDL, DML, joins and subqueries
    • Stored procedures and functions
    • Performance tuning and indexing
    • Interview-focused query practice

  2. Weeks 1–2 · Module 2

    Python (Data-Focused)

    • Variables, loops and functions
    • Pandas for data manipulation
    • Python in data pipelines
    • File I/O: CSV, JSON and Excel
    • Automation scripts for data tasks

  3. Weeks 3–4 · Module 3

    Azure Data Factory

    • ETL pipelines and orchestration
    • Linked services, datasets and activities
    • Parameterized pipelines
    • Scheduled, event and tumbling triggers
    • Pipeline monitoring and alerts

  4. Weeks 3–4 · Module 4

    Azure Data Lake Gen2

    • Storage concepts and hierarchy
    • File formats: CSV, Parquet and Delta
    • RBAC and access control
    • Integration with ADF and Databricks
    • Data lake best practices

  5. Weeks 3–4 · Module 5

    Databricks and PySpark

    • Workspaces and clusters
    • PySpark DataFrames and transformations
    • Delta Lake and Delta tables
    • Medallion architecture (Bronze, Silver, Gold)
    • Structured Streaming

  6. Weeks 5–6 · Module 6

    Azure Synapse

    • Synapse vs Fabric overview
    • Serverless and dedicated SQL pools
    • PolyBase and external tables
    • Synapse pipelines
    • Market awareness session

  7. Weeks 5–6 · Module 7

    Azure Logic Apps

    • Workflow automation basics
    • Trigger-based pipelines
    • Integration with ADF and email
    • Email alerts on pipeline failure
    • Real-world automation scenarios

  8. Weeks 5–6 · Module 8

    Microsoft Fabric

    • OneLake and Lakehouse concepts
    • Dataflows Gen2 (no-code ETL)
    • Fabric pipelines and activities
    • Fabric Warehouse and SQL
    • Power BI integration in Fabric

  9. Week 7 · Module 9

    AI Integration

    • ChatGPT for SQL and PySpark
    • AI for debugging pipelines
    • Prompt engineering basics
    • Copilot in Microsoft Fabric
    • AI-assisted data pipelines

  10. Weeks 7–8 · Module 10

    Projects and Placement Preparation

    • Project 1: ADF and SQL pipeline
    • Project 2: Fabric and AI end to end
    • ATS-format resume
    • LinkedIn and GitHub portfolio
    • Interview Q&A and mock interviews

  11. Throughout

    Data Warehouse Concepts

    • Fact and dimension tables
    • Star schema
    • Snowflake schema
    • Slowly Changing Dimensions: Types 1, 2 and 3

Keep the full curriculum with you

Verify your email and download this course as a PDF.

Projects you will build

Project 1: ADF and SQL End-to-End Pipeline

  • Ingest sales data from on-premises SQL Server
  • Transform with ADF Copy Activity and mapping
  • Load to Azure Data Lake Gen2 as Parquet
  • Incremental delta load strategy
  • Load to a Synapse SQL Pool for reporting
  • Email alert through Logic Apps on failure
  • Parameterized pipelines, with version control in Azure DevOps

Tools: SQL Server, ADF, ADLS, Synapse, Logic Apps

Project 2: Microsoft Fabric and AI Integration

  • Ingest multi-source data into OneLake
  • Build a Bronze, Silver and Gold Lakehouse
  • Dataflows Gen2 for no-code transformation
  • Fabric Warehouse SQL for analytics queries
  • ChatGPT-assisted PySpark transformation
  • AI Copilot for Power BI reports
  • Real-time analytics dashboard, with GitHub-ready documentation

Tools: Microsoft Fabric, AI Copilot, Power BI, PySpark

These are training projects. Please describe them honestly as training work on your resume and in interviews.

Tools and technologies

  • SQL Server
  • SQL
  • Python
  • PySpark
  • Azure Data Factory
  • Azure Databricks
  • Azure Data Lake Gen2
  • Azure Synapse
  • Azure Logic Apps
  • Microsoft Fabric
  • ChatGPT
  • Copilot

How you will learn

  • Weekend and weekday batches.
  • Live and recorded sessions.
  • Weekly assignments and tests.

Career support

  • ATS-format resume
  • LinkedIn optimization
  • GitHub portfolio
  • Interview Q&A practice
  • Mock interviews with feedback
  • Guidance for AZ-900, DP-900 and DP-700
  • Placement assistance

MYP does not guarantee a job or interviews.

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