A practical guide for data engineers adopting AI — covering core concepts, infrastructure decisions, and hands-on strategies to build AI-ready data platforms.
Data engineering is becoming the foundational layer every AI initiative depends on. Understand the shift from executor to orchestrator, and why the teams that recognize this moment will define what high-performing data operations look like for the next decade.
Learn AI essentials in a weekend. Build your first RAG app, understand LLMs and prompt engineering, explore AI agents, vector databases, and the Model Context Protocol.
Build real production AI skills in four weeks: advanced RAG techniques, multi-agent frameworks with LangGraph and CrewAI, LLMOps monitoring, and fine-tuning with LoRA and QLoRA.
For the truly ambitious data engineer. Dive into research papers, multimodal systems, production deployment patterns, evaluation frameworks, and reasoning models like DeepSeek-R1.
Curated AI resources for data engineers: top courses from DeepLearning.AI and Fast.ai, essential GitHub repos, YouTube channels, communities, documentation, and progress milestones to track your growth.
Where to start, what to build in the first week, and the infrastructure question your team will be answering by 2027.