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Data Engineering

A broad, production-minded data engineering course built from database foundations through SQL, integrity, ETL, Python pipeline construction, warehousing, performance, and cloud platform thinking. It reflects the multi-phase course already outlined in the data-engineering folder.

6 weeks 18 lessons 6 enrolled 4.8 rating

Live access status

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Who This Is For

  • Learners who want to understand how data moves from source to warehouse to analysis.
  • Analysts or scientists who need stronger SQL, ETL, and pipeline thinking.
  • Future data engineers building a serious foundation before cloud-heavy specialization.

How You'll Train

  • Move from database foundations into SQL, transactions, ETL, Python pipelines, warehousing, and operations.
  • Use failure-oriented labs to show what breaks when schemas, pipelines, or assumptions are weak.
  • Treat data work as systems work, not only as notebook analysis.

Technologies

PythonApache SparkAirflowdbtPostgreSQL

What You Leave With

  • Understand relational modeling, querying, and integrity at a deeper level.
  • Build simple end-to-end ingestion and transformation pipelines with Python and SQL.
  • Reason more confidently about warehousing, analytics engineering, and operational data workflows.

Portfolio Outcome

An end-to-end data pipeline project that moves source data into a warehouse-style analytical output.

Curriculum

Relational Model, Constraints, and Schema DesignLecture
60 min
Client-Server, Storage, and Query Planner LabLab
120 min
Production Incident SimulationAssignment
180 min

Instructor

I
IODS Faculty
Curriculum delivered by practising data & ML engineers