Data Engineering Learning Path

From Python scripts to trusted real-time platforms.

A focused, hands-on curriculum for engineers building reliable batch, distributed, streaming, and event-driven data systems.

Explore the curriculumHow the path works

Your learning path

5 stages
Python + Quality
Trusted foundationsKafka + Spark
Production systems
5technical courses
15hands-on units
45focused lessons
1coherent path
The curriculum

Build each layer in the right order

PY
Beginner

Python for Data

Build reliable data workflows with Python, pandas, NumPy, and production-grade coding practices.

24 hours3 units
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DA
Intermediate

Data Quality

Build trustworthy data products with profiling, contracts, automated validation, and quality observability.

22 hours3 units
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AP
Intermediate

Apache Spark

Process large datasets with Spark SQL, DataFrames, partitioning, and performance-aware transformations.

30 hours3 units
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AP
Advanced

Apache Kafka

Build event-driven systems with Kafka topics, partitions, consumer groups, delivery semantics, and schemas.

28 hours3 units
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Engineering-first learning

Understand, implement, operate

01

Learn the model

Understand execution, quality, state, delivery guarantees, and failure modes.

02

Build the system

Implement guided labs with realistic datasets and infrastructure.

03

Make it production-ready

Test, monitor, optimize, and reason about operational trade-offs.

Start with Python. Build trust. Scale to streaming.

Follow the complete path or jump into the module that matches your current level.

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