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Watch Gemini 3 Flash transform manual ETL into a near real-time workflow. This demo shows how the model’s speed and reasoning capabilities instantly resolve mismatched sources, clean messy data, and fully automate the pipeline.

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Google AI Developers

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65,671 görüntüleme • 7 ay önce •via X (Twitter)

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In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

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Here are a couple examples of how Gemini 3.1 Flash-Lite can solve real-world problems: First, this high-volume image sorter showcases the model’s ability to quickly analyze and sort large amounts of content, like pictures (something that could have been too expensive or slow in the past). This demo is just a snapshot 📸😉 of what can be built using 3.1 Flash-Lite's multimodal analysis capabilities. Think: real-time data visualization agents, CRM management tools, automated content moderation software, and beyond.

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