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Azure Data Engineer Roadmap

Azure Data Engineer Roadmap

1. Foundations

1.1 Cloud & Azure Fundamentals

  • AZ-900: Microsoft Azure Fundamentals
    • Core concepts: IaaS vs PaaS vs SaaS
    • Azure global infrastructure
    • Core services: Compute, Storage, Networking
  • Resources

1.2 Data Fundamentals


2. Core Data Engineering Services

ServiceRoleSuggested Learning Path
Azure StorageBlob Storage, ADLS Gen2Quickstarts + hands-on lab on Microsoft Docs
Azure SQL DatabaseManaged relational databaseProvision & query tutorials
Azure Data FactoryETL/ELT orchestrationBuild sample pipelines
Azure DatabricksApache Spark analyticsIntro notebooks + “Data Engineering with Spark” modules
Azure Synapse AnalyticsUnified analytics (SQL + Spark + Pipelines)Synapse workspace labs + serverless SQL
Event Hubs / IoT HubHigh-throughput data ingestionEnd-to-end event-driven pipeline
Azure Stream AnalyticsReal-time stream processingReal-time dashboard over sensor data

Hands-on approach for each service:

  1. Follow the official “Quickstart” on docs.microsoft.com.
  2. Complete a guided lab in Microsoft Learn or GitHub.
  3. Build a mini-project (e.g. ingest CSV → transform in Databricks → load to Synapse → visualize in Power BI).

3. Certification & Deep Dives

  • DP-203: Data Engineering on Microsoft Azure
    • Design & implement data storage solutions
    • Develop data processing solutions (batch & streaming)
    • Secure and monitor data solutions
  • Optional Advanced Exams
    • DP-500 (Azure Database Administrator)
    • AZ-304/305 (Azure Solutions Architect – Data focus)

4. Advanced Topics & Best Practices

  1. Infrastructure as Code
    • ARM templates, Bicep, Terraform
  2. DevOps for Data
    • CI/CD pipelines for Data Factory & Databricks (Azure DevOps or GitHub Actions)
  3. Security & Governance
    • Azure Key Vault, RBAC, Azure Policy & Blueprints
  4. Performance & Cost Optimization
    • Spark tuning, SQL pool indexing, storage/compute tiering
    • Budgets, cost alerts

5. Build Real-World Projects

  1. Data Lake Ingestion
    • Simulate IoT data → ADLS Gen2 → catalog with Purview
  2. End-to-End Analytics
    • Sales pipeline → Synapse SQL pool → Power BI dashboard
  3. Streaming Analytics
    • Clickstream / telemetry → Event Hubs → Stream Analytics → Cosmos DB

Tip: Publish each project to GitHub to showcase your skills.


6. Community & Ongoing Learning

  • Blogs & Newsletters
    • Data Engineering on Azure blog
    • Azure Weekly newsletter
  • Forums & Meetups
    • StackOverflow [azure-data-factory]
    • Local Azure / Data Engineering meetups (search Meetup.com for HCMC)
  • Hackathons & Practice
    • Microsoft Data Saturdays
    • Kaggle competitions

7. Suggested 12-Week Study Plan

WeekFocus Area
Weeks 1–2AZ-900 & DP-900 (Foundations)
Weeks 3–4Azure Storage & SQL Database
Weeks 5–6Azure Data Factory (ETL/ELT patterns)
Weeks 7–8Azure Databricks & Spark
Week 9Azure Synapse Analytics
Week 10DP-203 Exam Prep & Practice Tests
Weeks 11–12Capstone Project & GitHub Portfolio
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