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AI için Veri Mühendisliği

How did data engineering evolve from 1995 to 2026? Differences between DBA, ETL Developer, Data Engineer, Analytics Engineer and AI Data Engineer; skill matrix; Turkey & global salary ranges; daily workflow.

1 module
3 lessons
~130 min

How this learning category is structured

Each category is a progressive chain of modules — from foundational concepts to production-grade architectural choices. Following the sequence is faster, but every module is self-contained.

Module shape is consistent: a short text/video lesson (10–15 minutes), a hands-on example (code + data), a 10–15 question assessment, and a real-world use case anchor. This structure forecloses the 'I saw it, I get it' trap — the assessment-after-application tests whether the concept actually moved into working memory.

Each category emphasizes production-grade practice: in prompt engineering, not just prompt templates but prompt versioning and A/B testing; in RAG, not just chunk-and-embed but hybrid retrieval + reranker + evaluation; in LLMOps, not just deployment but observability and cost attribution.

Recommended path: complete foundational modules in order first, then selectively consume advanced modules based on need. If you prefer cohort format, drip-release paces you with peers; in self-paced mode you control the cadence.

  • Each module: 10–15 minute lesson + hands-on example + assessment.
  • Production-oriented; lessons anchor in real vendor/tooling choices.
  • Modules are independently consumable, but the sequence accelerates retention.
  • Pro membership unlocks certificate exam + AI tutor + drip cohort access.

Table of Contents

Module 0: Introduction & Local Stack

Frequently Asked Questions

  • Modules are designed to be followed in the order shown in the table of contents. The first module lays the groundwork, later ones build on it. You can skip a section, but if a 'Prerequisites' block appears in a side module, complete those lessons first.