Data Engineering

An industry-aligned journey from foundations to production-style pipelines.

· Python/SQL → Spark → Cloud → Orchestration → Capstone

Who it's for: Learners seeking a structured path into data engineering with Python, SQL, big data, cloud and orchestration.

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Course highlights

Python and advanced SQL foundations
Warehousing, dimensional modeling and ETL/ELT
Apache Spark/PySpark for scale
Airflow orchestration and Azure Data Factory
dbt transformations and Kafka streaming fundamentals
Data quality, governance and security
End-to-end production-style capstone

Detailed syllabus

Scripting
Data structures
APIs
Files

8 more modules in this curriculum

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What you'll walk away with

Practical skills and learning outcomes

Practical skills

Python scripting
Advanced SQL
Data modeling
ETL/ELT design
PySpark
Airflow
Azure Data Factory
dbt
Kafka
Data quality and governance

Learning outcomes

Build and explain end-to-end pipelines
Use Python and SQL as core tools
Process data at scale with Spark
Orchestrate workflows with Airflow
Build cloud workflows with ADF
Apply transformation, quality and governance practices
Deliver an industry-style capstone

Capstone project

Production-style end-to-end pipeline: ingestion → transformation → warehouse → dashboard-ready output, run through two-week Jira sprints with a midpoint stakeholder review and a final demo.

Career outcomes

Data Engineer
Junior Data Engineer
Cloud Data Engineering roles
ETL / Data Pipeline roles

What sets this course apart

Combines SQL/Python foundations with Spark, one cloud platform, orchestration and transformation tooling. Azure is the primary cloud; AWS Glue and GCP BigQuery are introduced as alternatives.

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Begin your journey

Real mentors. Live enterprise work. A path into SAP, Power BI, and Power Platform roles—not another certificate on the shelf.

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