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AI-Integrated Data Engineering course with live classes

Azure data engineering with generative AI built in. Learn SQL, Python, Azure Data Factory, Databricks, PySpark and Power BI in five months, use AI assistants safely, then join it all into one pipeline you built yourself.

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5 months · 11 modules · Batches of 20 · Weekend batches available

pipeline.py · PySpark
# bronze: raw data landsbronze = spark.read.json("abfss://raw@lake/orders/")# silver: cleaned and validatedsilver = bronze.dropDuplicates(["order_id"])silver.write.format("delta").saveAsTable("silver.orders")✓ bronze loaded✓ silver validated✓ gold ready for Power BI✓ tests generated and reviewed

AI-Integrated Data Engineering course at a glance

Data Fab's AI-Integrated Data Engineering course is a five-month, live, instructor-led program that teaches Azure data engineering with generative AI built in: SQL and T-SQL, Python, Azure Data Factory, Databricks, PySpark, Delta Lake, Power BI and AI-assisted coding. You finish with a secured, monitored capstone pipeline. No prior data engineering experience is needed.

course.describe()
Course facts
duration 5 months
format Live, instructor-led classes
revision Recorded revision class after every module
batch_size 20 students
timings Flexible, with weekend batches
prerequisites None. Starts from SQL and Python basics
tools SQL, Python, Azure Data Factory, ADLS Gen2, Databricks, PySpark, Delta Lake, Power BI, Git, Key Vault, Azure OpenAI
project End-to-end capstone pipeline
ai AI-assisted SQL, Python and PySpark, testing and troubleshooting
support Interview preparation, mock interviews, job-portal profile help
licences Free-tier tools, no paid licences

Who this course is for

Three starting points, one path to the same project.

  • Freshers

    You start

    Little or no data engineering experience

    How we help

    SQL and Python from the basics, built up module by module

    You finish

    A real capstone project to show in interviews

  • Working professionals

    You start

    Working in IT, BI or another role and looking to switch

    How we help

    Weekend batches, flexible timings and the tools job postings ask for

    You finish

    A portfolio pipeline that mirrors production work

  • Career-gap returners

    You start

    A break from work, and unsure where to restart

    How we help

    A recorded revision class after every module, at your own pace

    You finish

    Rebuilt skills and confidence, ready for interviews

Not sure which one you are? Ask our team and we will help you decide.

What you'll be able to do

  • Write production-grade SQL and Python for data pipelines
  • Build and orchestrate pipelines in Azure Data Factory
  • Process big data at scale with PySpark on Databricks
  • Design a lakehouse with Delta Lake and the medallion architecture
  • Secure and monitor pipelines like a real production system
  • Build Power BI dashboards on your own pipeline output
  • Use generative AI to write, review and test pipeline code safely
  • Explain a complete end-to-end project in interviews

The curriculum: eleven modules, one pipeline

Scroll through the modules and see where each one fits in a real pipeline. Module 1 maps the whole pipeline and module 11 builds it end to end. Blue is Learn (modules 1 to 4), teal is Build (5 to 8), green is Grow (9 to 11).

Where each module fits. The data lake sits beside every stage because each one reads from it or writes to it.
  1. SourcesREST APIs, SQL databases, filesModules 2, 3
  2. Data lake (ADLS Gen2)Bronze, silver and gold zonesModule 4
  3. Azure Data FactoryOrchestrated ingestionModule 5
  4. DatabricksPySpark and Delta LakeModules 6, 7
  5. Power BIReports on the gold layerModule 8

Production practicesGit, security, monitoring, data qualityModule 9

AI assistanceGenerative AI for code, tests and troubleshootingModule 10

  1. Stage: Whole pipeline

    Introduction to Data Engineering

    What a data engineer does, the tools you'll master and the roadmap ahead.

    • Role and lifecycle
    • ETL vs ELT
    • Batch vs stream
    • Lake, warehouse, lakehouse

  2. Stage: Sources and transformation

    SQL & T-SQL

    From first query to advanced joins, window functions and data modeling.

    • Joins
    • CTEs
    • Window functions
    • Stored procedures
    • Indexes and execution plans
    • Star schema and SCD

  3. Stage: Sources and transformation

    Python for Data Engineering

    Scripting, file and API handling, and pandas.

    • pandas
    • CSV, JSON, Parquet
    • REST APIs
    • Logging and error handling

  4. Stage: Storage

    Azure Fundamentals & Data Lake Storage

    Hands-on cloud basics and a production-ready data lake design.

    • Resource groups
    • Entra ID and RBAC
    • ADLS Gen2
    • Raw, processed, curated zones
    • Cost control

  5. Stage: Ingestion

    Azure Data Factory

    Real pipelines: ingestion, transformation, triggers and incremental loads.

    • Pipelines and activities
    • Triggers
    • Mapping data flows
    • Incremental loads
    • Metadata-driven frameworks

  6. Stage: Processing

    PySpark in Databricks

    Big data processing, performance tuning, Delta Lake and the medallion architecture.

    • Spark architecture
    • DataFrames
    • Joins and partitions
    • Delta Lake
    • Bronze, silver, gold

  7. Stage: Processing

    Azure Databricks

    Spark on the Azure-integrated, enterprise-ready platform.

    • Clusters and cost
    • Managed identity
    • Key Vault secrets
    • Unity Catalog
    • Workflows and jobs

  8. Stage: Serving

    Power BI

    Dashboards and reports on top of your pipeline's output.

    • Power Query
    • Star-schema models
    • DAX basics
    • Reports and drill-through

  9. Stage: Production

    Production Practices

    Git, security, monitoring and data quality.

    • Git and GitHub
    • CI/CD basics
    • Monitoring and alerts
    • Data quality checks
    • Governance basics

  10. Stage: AI assistance

    AI-Assisted Data Engineering

    Use generative AI to write, explain, test and troubleshoot data pipeline code, and learn to review it safely and responsibly.

    • Generative AI fundamentals
    • AI-assisted SQL
    • AI-assisted Python and PySpark
    • Explaining and debugging code
    • Unit tests and validation scripts
    • Azure AI and Azure OpenAI
    • Reviewing AI-generated code
    • Responsible AI and data privacy

  11. Stage: End to end

    Capstone Project

    One end-to-end pipeline, built, secured and presented like a real project.

    • REST/SQL to ADLS bronze
    • Silver and gold Delta tables
    • Power BI on gold
    • Portfolio and interview bank

AI built into the course, not bolted on

Data engineering teams increasingly use AI assistants. Module 10 teaches you to use them well, and to know when not to trust them.

ai-assist · review
# prompt"Latest order per customer in T-SQL"# AI draftSELECT * FROM (SELECT *, ROW_NUMBER() OVER  (PARTITION BY customer_id ORDER BY order_date DESC) rnFROM orders) t WHERE rn = 1;# your review✓ correct  ✓ tested  ✓ no sensitive data shared

What the AI module covers

  • Generative AI fundamentals for data engineers
  • AI-assisted SQL query generation and optimization
  • AI-assisted Python and PySpark code generation
  • Explaining existing code and debugging errors with AI
  • Generating unit tests and data validation scripts
  • AI-assisted documentation and pipeline troubleshooting
  • Introduction to Azure AI services and Azure OpenAI
  • AI-assisted data processing workflow examples
  • Reviewing AI-generated code for correctness and security
  • Responsible AI, data privacy and handling sensitive data

Capstone: one pipeline, end to end

In the final module you build the pipeline below, secure it with managed identity and Key Vault, monitor it, and present it like a real project.

  1. SourcesREST API and SQL
  2. Azure Data FactoryMetadata-driven pipelines
  3. ADLS bronzeRaw data lands
  4. DatabricksSilver and gold Delta tables
  5. Power BIReport on the gold layer
capstone · run
$ capstone run --env prod✓ ingest  REST API and SQL via metadata-driven ADF✓ bronze  raw data landed in ADLS Gen2✓ silver  PySpark cleans, validates, MERGE with SCD logic✓ gold    Delta tables ready for reporting✓ serve   Power BI report refreshed✓ secure  managed identity, Key Vault, monitoring, quality checks

What you leave with

  • A full pipeline from REST API and SQL sources to a Power BI report
  • Secured with managed identity and Key Vault
  • Monitored with alerts, audit logs and data quality checks
  • An architecture diagram you can present
  • A portfolio write-up of the project
  • An interview question bank

Tools you'll use

The same tools data engineering teams list in their job postings.

  • SQL and T-SQLModule 2
  • PythonModule 3
  • ADLS Gen2Module 4
  • Azure Data FactoryModule 5
  • PySparkModule 6
  • Delta LakeModule 6
  • Azure DatabricksModule 7
  • Power BIModule 8
  • Git and GitHubModule 9
  • Key VaultModule 9
  • Azure OpenAIModule 10

Career support included

Preparation for the interview, not just the syllabus.

Interview preparation and mock interviews with feedback, plus guidance on setting up and polishing your Naukri and LinkedIn profiles.

Ask about career support

What our career support covers

5 areas
  • A capstone project you can explain end to end
  • Interview questions practised
  • Mock interviews with feedback
  • Naukri profile set up
  • LinkedIn profile polished

Course questions

Straight answers before you enquire.

Still have a question?

Send us your details and our team will get back to you.

Ask us
What will I learn in the AI-Integrated Data Engineering course?

You will learn SQL and T-SQL, Python, Azure Data Factory, Databricks with PySpark, Delta Lake, Power BI, production practices and AI-assisted data engineering, then build one end-to-end capstone pipeline.

What does the AI part of the course cover?

Module 10 covers generative AI fundamentals, AI-assisted SQL, Python and PySpark, explaining and debugging code, generating tests and validation scripts, documentation and troubleshooting, Azure AI services and Azure OpenAI, plus reviewing AI-generated code and handling data responsibly.

Is this still an Azure data engineering course?

Yes. The core is Azure data engineering with Data Factory, Databricks, ADLS Gen2 and Power BI. Generative AI is added on top, so you learn to use AI assistants safely in real pipeline work.

Who is this course for?

Freshers, working professionals moving into data engineering, and career-gap returners. No prior data engineering experience is needed.

Do I need programming experience?

No. The course starts with SQL and Python fundamentals and builds up module by module.

How long is the course and what are the timings?

The course runs for five months. Timings are flexible, and weekend batches are available for working professionals.

Are the classes live?

Yes. Every session is live with an instructor, and each module also has a recorded revision class you can replay anytime.

Do I need to pay for Azure or other software?

The course uses free-tier and free-account tools throughout, so no paid licences are needed to follow along.

Will I build a project?

Yes. The capstone is a full pipeline from REST API and SQL sources through Azure Data Factory and Databricks to a Power BI report, built, secured and presented like a portfolio piece.

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