Morphera Lab · Data Engineering Curriculum

The Cloud Data Engineer

A practical, chaptered book for learning data engineering — from mindset and SQL through Python, orchestration, engineering practices, AI-assisted workflows, streaming, and cloud platforms.

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Morphera
Lab

SQL

Python

Mindset

Airflow

Docker

Snowflake

AWS / Azure

Kafka

AI Agents

Why Morphera Lab

Built for the Modern Engineer

We don't just teach syntax. We teach you how to think like a senior engineer and build robust, scalable, intelligent data systems.

Industry-Standard Stack

Learn the exact tools used at top tech companies — Snowflake, dbt, Airflow, Kafka, Docker, and modern cloud environments.

Project-Based Learning

Move beyond tutorials. Build end-to-end data pipelines that mimic real-world business challenges.

AI-Integrated Workflows

Stay ahead of the curve. Learn to leverage coding agents and LLMs to accelerate your data engineering workflows.

Curriculum

The Complete Roadmap

43 chapters across 10 parts. A clear, structured path from absolute fundamentals to advanced cloud architectures.

Mindset

Thinking Like a Data Engineer

Build the mindset and systems view needed before going deep into tools.

Foundations

SQL: The Language of Data

Learn querying, joins, views, aggregation, CTEs, windows, pivots, and schema design.

Programming

Python: The Engineer's Tool

Build programming fluency for files, errors, NumPy, Pandas, and data workflows.

Pipelines

Orchestration: Apache Airflow

Understand workflow orchestration, DAGs, scheduling, and production pipeline concepts.

DevOps

Engineering Practices

Use CI/CD, automation, container practices, and delivery workflows as a data engineer.

Agentic Data Engineering

Explore AI-assisted workflows, coding agents, and agentic data pipeline patterns.

Warehousing

Cloud Warehousing: Snowflake + dbt

Learn Snowflake architecture, loading patterns, advanced features, and dbt production workflows.

Streaming

Real-Time Data: Apache Kafka

Learn event streaming fundamentals and the role Kafka plays in real-time pipelines.

Cloud

AWS for Data Engineers

Study IAM, cloud data infrastructure, serverless processing, and AWS data services.

Cloud

Azure for Data Engineers

Learn Azure storage foundations and enterprise data platform building blocks.

Why This Book

Built for readable technical learning

No practical context to real systems?

Every chapter is grounded in real-world engineering scenarios. We show you not just how a tool works, but exactly where and why you'd use it in production — from ingestion pipelines to monitoring dashboards.

Production-Ready

Scattered resources, no clear progression?

The book is structured as a numbered curriculum — 43 chapters across 10 sequential parts. You always know where you are, what comes next, and why. No more jumping between disconnected tutorials.

Structured Path

Tools evolve. Does your knowledge keep up?

We cover the modern AI-integrated engineer. Part 5 is dedicated entirely to agentic workflows, LLM-assisted pipelines, and using coding agents to automate data engineering tasks — skills that are defining the next era.

Future-Proof

Learn from the Best

Lead Instructors

Guided by industry veterans who have built data platforms at scale. We condense years of production experience into practical, battle-tested lessons.

Sir Qasim Hasan

Lead Data Architect

Specializes in cloud infrastructure, advanced orchestration, and scalable data warehousing architectures. Brings years of production experience building data platforms for enterprise clients.

Sir Ayan

Senior Data Engineer

Expert in streaming technologies, Python development, and integrating AI into modern data pipelines. Passionate about making complex engineering concepts accessible and practical.

Ready to build your Data Career?

Join thousands of learners mastering the modern data stack. Start with the published lessons already in the book — free, today.

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