23. Vector ETL: Integrating Data Lakes & Vector Databases for Scalable AI
Your raw data is in a Data Lake (S3, Azure, GCS), but your AI application needs vectors. This video is your complete guide to building a production-ready Vector ETL Pipeline! We break down the Extract, Transform (Chunking & Embedding), and Load workflow, showcase best practices like incremental processing and model versioning, and provide real-world Python code examples using frameworks like Milvus and Pinecone. Learn how to scale your RAG and semantic search systems to millions of documents.
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