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GenAI for SQL & ETL:大规模构建多模式 AI 工作流.pdf

上传人: Fl****zo 编号:718652 2025-06-22 41页 3.69MB

1、Forward-looking StatementThis presentation has been prepared for informational purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the date of this presentation unless stated otherwise.This pres

2、entation and the accompanying oral commentary may contain forward-looking statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“believes”,“estimates”,“predicts”,or“continue”,or the neg

3、ative of these words or other similar terms or expressions that concern Databricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently subject to risks and uncertainties that could cause actu

4、al results to differ materially from those expressed in or suggested by the forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not undertake any obligation to publicly update or revise an

5、y forward-looking statement,whether as a result of new information,future developments or otherwise.2GenAI for SQL&ETL:Build Multimodal AI Workflows at ScaleBilal(Staff Product Manager)Colton(Staff Data Scientist)Ever been asked to pull insightsfrom a PDF?An email thread?Asupport folder?4What%of ent

6、erprise data isunstructured?590%of enterprise data is unstructuredUNSTRUCTURED DATASTRUCTURED DATA20252005201020152020Source:IDC175ZBBut whats actually in that 90%?Support ticketsContractsCustomer reviewsEmailsProduct photosCall recordingsTraining videosUnstructured Enterprise DataTicket summarizati

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本文主要内容是介绍Databricks公司如何利用AI函数和Lakeflow技术,帮助企业在处理大量非结构化数据时,实现类似处理结构化数据般的便捷性。核心数据指出,企业中90%的数据为非结构化数据。以下是关键点: 1. 非结构化数据增长迅速,包括支持工单、合同、客户评论等多种形式。 2. Databricks推出AI函数,只需一行SQL或Python代码,即可在保留治理和血缘关系的前提下,提取非结构化数据中的洞察。 3. AI函数与Lakeflow结合,形成GenAI ETL,简化了从数据摄取到转换再到运行的全流程,具备自动扩展和安全性。 4. 提供多种AI任务特定函数和通用ai_query,优化模型性能评估,支持多语言任务和成本控制。 5. 鼓励用户尝试使用AI函数,并提供了易于上手的操作方法和生产环境应用指导。
"90%数据如何利用?" "AI赋能,ETL流程怎么革新?" "如何一键解析复杂数据?"
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