Ultimate PostgreSQL Bootcamp: Go from Beginner to Expert
From SELECT to production PostgreSQL mastery on 100k+ real e-commerce orders.
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Data engineers move data from where it lives to where it's useful. These courses focus on the Python-first tooling most AI-adjacent teams pick. There is no Spark-centric career track here. You'll learn pipelines, embeddings, RAG retrieval patterns, and the observability needed to trust what you ship.
Curated by Param Harrison
Every course leans practical: real datasets, real retrieval problems, real evaluation. If you've been piecing together AI systems from YouTube tutorials, this track gives you the scaffolding those videos skip.
Showing 7 of 7 courses
Common questions
Same core job (move and shape data) plus a new layer: embeddings, vector stores, retrieval quality, and eval infrastructure. Teams expect you to own the data path into LLM pipelines, not just analytical warehouses.
No. These courses stay Python-first (cron + scripts + typed pipelines) because that’s what most AI-focused teams run. Airflow and dbt are worth learning when you’re at a specific scale, but they’re not prerequisites here.
SQL gets you to analytical work. For AI pipelines you also need Python for embedding, chunking, retrieval, and evaluation. The SQL for Engineers course covers the SQL half; the RAG and Python courses cover the other half.
Data engineers own the pipeline (ingestion, cleaning, indexing). AI engineers own the model interaction (prompts, agents, tool use). In small teams one person does both.
Start with Python for GenAI, then RAG Fundamentals. After that, the observability course helps you prove the retrieval layer you built actually works.