Structured Career Curricula

Senior Data Engineering Learning Paths

12 comprehensive, week-by-week architectural roadmaps from Microsoft Fabric DP-600 to 32-Week Principal Lakehouse Mastery with capstone repositories and mock interview rubrics.

Curricula Tracks

1. Microsoft Fabric Analytics Engineer (DP-600)

Certified Path•8 Weeks Curriculum

Master Lakehouse architecture, OneLake, Direct Lake mode, Delta Lake optimization, Data Factory pipelines, and Fabric Admin capacity management.

Target Outcome

Enterprise Retail Analytics Lakehouse with Direct Lake Reporting & Capacity Guardrails

Core Skills & Protocols Mastered:

Direct LakeOneLake ShortcutsV-Order DeltaFabric Data FactoryDAX / TabularSemantic Models

Phase-by-Phase Roadmap Timeline:

Phase 1: OneLake & Lakehouse Foundations

Weeks 1-2

Topics Covered:

OneLake Architecture
Lakehouse vs Warehouse in Fabric
Internal vs External Shortcuts
Delta Log Engine

Milestone:Provision Fabric capacity and configure workspace security, short-cuts, and medallion architecture.

Phase 2: High-Performance Data Ingestion & Transformation

Weeks 3-4

Topics Covered:

Data Factory Ingestion
Fabric PySpark Optimization
V-Order & OPTIMIZE Z-ORDER
Notebook Orchestration

Milestone:Build Copy Activity + Dataflow Gen2 pipelines feeding PySpark Notebooks with V-Order compression.

Phase 3: Semantic Modeling & Direct Lake Performance

Weeks 5-6

Topics Covered:

Direct Lake Framing
DAX Optimization
VertiPaq Memory Tuning
RLS / OLS in Fabric

Milestone:Design a billion-row Direct Lake semantic model with zero DAX fallback to import mode.

Phase 4: Capstone & DP-600 Exam Readiness

Weeks 7-8

Topics Covered:

Fabric Admin & Capacity Metrics
Git Integration & Deployment Pipelines
Mock Exam Rubric

Milestone:Complete end-to-end multi-tenant Fabric deployment and pass 100-question timed mock exam.

Hands-On Capstone Project

Fabric Multi-Source Lakehouse Ingestion

Build a production medallion lakehouse linking S3, Azure SQL, and Fabric OneLake with V-Order compression.

Repository: https://github.com/santoshjammi29/fabric-dp600-starter

Interactive Architecture Decision Ledger (ADR)

System Design Decision Points

Evaluate real-world trade-offs, record architectural choices, and generate portfolio-ready Technical Justification briefs.

Engineering Scenario:

Primary Lakehouse Storage & Table Format

Your enterprise platform ingests 4 TB/day of semi-structured events with high-concurrency BI dashboards querying a 50-billion-row gold layer.

Select Candidate Architecture:

Generated Architecture Decision Record (ADR)

Decision:Adopt Delta Lake 3.x + Liquid Clustering as primary tier standard.

Technical Rationale:We selected **Delta Lake with Liquid Clustering**. Liquid Clustering replaces rigid hierarchical partitioning with flexible clustering keys, preventing small-file proliferation and partition skew. Direct Lake integration ensures billion-row dashboards render without data duplication or import latency.

Architectural Benefits

• Eliminates partition skew and manual OPTIMIZE Z-ORDER maintenance
• Seamless Direct Lake mode with V-Order sub-second query performance
• Universal Format (UniForm) allows Iceberg clients to read Delta metadata

Trade-offs & Mitigations

• Liquid Clustering writes have slight overhead during concurrent micro-batch ingestion
• Requires modern Spark 3.5+ or Fabric Runtime 1.2+ for native support

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Senior Data Engineering Learning Paths

1. Microsoft Fabric Analytics Engineer (DP-600)

2. Azure Data Engineer Associate (DP-203)

3. Microsoft Power BI Data Analyst (PL-300)

4. Apache Spark 3 → 4 Engineering & Optimization

5. Databricks Lakehouse & Photon Vectorized Engine

6. Snowflake SnowPro Advanced Architect

7. Google BigQuery & GCP Data Engineering

8. Open Table Formats (Delta / Iceberg / Hudi / Paimon)

9. Real-Time Streaming & Log-Based CDC Architecture

10. AI / LLM Data Engineering & RAG Architecture

11. Serverless Cost Engineering & FinOps Mastery

12. Enterprise Data Architecture Mastery (32-Week Track)

1. Microsoft Fabric Analytics Engineer (DP-600)

Core Skills & Protocols Mastered:

Phase-by-Phase Roadmap Timeline:

Phase 1: OneLake & Lakehouse Foundations

Phase 2: High-Performance Data Ingestion & Transformation

Phase 3: Semantic Modeling & Direct Lake Performance

Phase 4: Capstone & DP-600 Exam Readiness

Fabric Multi-Source Lakehouse Ingestion

System Design Decision Points

Primary Lakehouse Storage & Table Format

Delta Lake 3.x + Liquid Clustering

Apache Iceberg + Snowflake/Trino Engine

Microsoft Fabric Synapse Data Warehouse

Senior Data Engineering Learning Paths

1. Microsoft Fabric Analytics Engineer (DP-600)

2. Azure Data Engineer Associate (DP-203)

3. Microsoft Power BI Data Analyst (PL-300)

4. Apache Spark 3 → 4 Engineering & Optimization

5. Databricks Lakehouse & Photon Vectorized Engine

6. Snowflake SnowPro Advanced Architect

7. Google BigQuery & GCP Data Engineering

8. Open Table Formats (Delta / Iceberg / Hudi / Paimon)

9. Real-Time Streaming & Log-Based CDC Architecture

10. AI / LLM Data Engineering & RAG Architecture

11. Serverless Cost Engineering & FinOps Mastery

12. Enterprise Data Architecture Mastery (32-Week Track)

1. Microsoft Fabric Analytics Engineer (DP-600)

Core Skills & Protocols Mastered:

Phase-by-Phase Roadmap Timeline:

Phase 1: OneLake & Lakehouse Foundations

Phase 2: High-Performance Data Ingestion & Transformation

Phase 3: Semantic Modeling & Direct Lake Performance

Phase 4: Capstone & DP-600 Exam Readiness

Fabric Multi-Source Lakehouse Ingestion

System Design Decision Points

Primary Lakehouse Storage & Table Format

Delta Lake 3.x + Liquid Clustering

Apache Iceberg + Snowflake/Trino Engine

Microsoft Fabric Synapse Data Warehouse