How can I design a scalable data pipeline for real-time AI model training ?

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How can I design a scalable data pipeline for real-time AI model training when dealing with both structured and unstructured data sources?

I'm currently building an AI system that needs to process large volumes of structured (SQL databases) and unstructured (social media text, images) data in near real-time. I’m struggling to design a data engineering pipeline that can handle diverse data types efficiently while feeding my machine learning models with clean, ready-to-train data.
What tools, architecture patterns, or best practices would you recommend for scalability, low latency, and fault tolerance? Any real-world examples or tech stacks would be super helpful!