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Better integrate Spark SQL with ETL version 6 for distributed & batch scalable ETL Pipeline Processing
enhancementNew feature or requestNew feature or requestETLData processing / ETL pipelines, DML generators, processes between crawl, scrape, APIs, ingestData processing / ETL pipelines, DML generators, processes between crawl, scrape, APIs, ingestStatus: Open.#5 In ChiefsBestPal/BooksScrapeDatabase_and_Recommender;Fully integrate and leverage Spark's GraphX (Graph processing) with Neo4j graph engine & plugins
enhancementNew feature or requestNew feature or requestStatus: Open.#4 In ChiefsBestPal/BooksScrapeDatabase_and_Recommender;Allow longer book summaries/synopsis either as attributes or Junction/Bridge tables for better NLP context
Book dataBibliography, links, editions, characters, settings, subjects, genres, series, relevant metadataBibliography, links, editions, characters, settings, subjects, genres, series, relevant metadataenhancementNew feature or requestNew feature or requestETLData processing / ETL pipelines, DML generators, processes between crawl, scrape, APIs, ingestData processing / ETL pipelines, DML generators, processes between crawl, scrape, APIs, ingestStatus: Open.#3 In ChiefsBestPal/BooksScrapeDatabase_and_Recommender;Benchmark diminishing returns: quantized local RAG VS Cloud AI platform / LLM tokens
AI LLM integration & RAGEmbeddings, vector search, Graph RAG, ctx config, token optim, LLMchains, ML E2E , LLM modelsEmbeddings, vector search, Graph RAG, ctx config, token optim, LLMchains, ML E2E , LLM modelsquestionFurther information is requestedFurther information is requestedStatus: Open.#2 In ChiefsBestPal/BooksScrapeDatabase_and_Recommender;Finish private cloud and push here for warehouse native support of GCP BigQuery, Dataflow
GCP / CloudDirect work or issues to be done with native Cloud services. More specifically GCPDirect work or issues to be done with native Cloud services. More specifically GCPStatus: Open.#1 In ChiefsBestPal/BooksScrapeDatabase_and_Recommender;