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
- Azure Fundamentals learning path (Microsoft Learn)
- “Azure Fundamentals” video series by John Savill (YouTube)
1.2 Data Fundamentals
- DP-900: Microsoft Azure Data Fundamentals
- Relational vs non-relational data
- Batch vs streaming vs real-time
- Big Data vs analytics
- Resources
- Azure Data Fundamentals learning path (Microsoft Learn)
- Hands-on sandbox labs in Microsoft Learn
2. Core Data Engineering Services
| Service | Role | Suggested Learning Path |
|---|---|---|
| Azure Storage | Blob Storage, ADLS Gen2 | Quickstarts + hands-on lab on Microsoft Docs |
| Azure SQL Database | Managed relational database | Provision & query tutorials |
| Azure Data Factory | ETL/ELT orchestration | Build sample pipelines |
| Azure Databricks | Apache Spark analytics | Intro notebooks + “Data Engineering with Spark” modules |
| Azure Synapse Analytics | Unified analytics (SQL + Spark + Pipelines) | Synapse workspace labs + serverless SQL |
| Event Hubs / IoT Hub | High-throughput data ingestion | End-to-end event-driven pipeline |
| Azure Stream Analytics | Real-time stream processing | Real-time dashboard over sensor data |
Hands-on approach for each service:
- Follow the official “Quickstart” on docs.microsoft.com.
- Complete a guided lab in Microsoft Learn or GitHub.
- 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
- Infrastructure as Code
- ARM templates, Bicep, Terraform
- DevOps for Data
- CI/CD pipelines for Data Factory & Databricks (Azure DevOps or GitHub Actions)
- Security & Governance
- Azure Key Vault, RBAC, Azure Policy & Blueprints
- Performance & Cost Optimization
- Spark tuning, SQL pool indexing, storage/compute tiering
- Budgets, cost alerts
5. Build Real-World Projects
- Data Lake Ingestion
- Simulate IoT data → ADLS Gen2 → catalog with Purview
- End-to-End Analytics
- Sales pipeline → Synapse SQL pool → Power BI dashboard
- 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)
- StackOverflow
- Hackathons & Practice
- Microsoft Data Saturdays
- Kaggle competitions
7. Suggested 12-Week Study Plan
| Week | Focus Area |
|---|---|
| Weeks 1–2 | AZ-900 & DP-900 (Foundations) |
| Weeks 3–4 | Azure Storage & SQL Database |
| Weeks 5–6 | Azure Data Factory (ETL/ELT patterns) |
| Weeks 7–8 | Azure Databricks & Spark |
| Week 9 | Azure Synapse Analytics |
| Week 10 | DP-203 Exam Prep & Practice Tests |
| Weeks 11–12 | Capstone Project & GitHub Portfolio |