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The Rise of Unstructured Data

Cloudera

The word “data” is ubiquitous in narratives of the modern world. And data, the thing itself, is vital to the functioning of that world. This blog discusses quantifications, types, and implications of data. Quantifications of data. Most of that data will be unstructured, and only about 10% will be stored.

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Building a Data-Centric Platform for Generative AI and LLMs at Snowflake

Snowflake

LLMs have the potential to help both developers and less-technically inclined users make sense of the world’s data. Protecting sensitive or proprietary data such as source code, PII, internal documents, wikis, code bases, and other sensitive data sets, along with prompts, used to contextualize the LLMs is particularly important.

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GPT and LLMs from a Data Engineering Perspective

Jesse Anderson

The metric I use for technology adoption is, what would people say if it were to disappear tomorrow? that summarizes blog posts using LLMs. I think LLMs will allow the majority to get stupider, like in Idiocracy , while a gradually decreasing population will benefit and exploit AI (and technology in general).

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Gen AI Perspectives from Industry Leaders Shaping the Future

Snowflake

It started when one capable model suited for text gained mainstream attention, and now, less than 18 months later, there is a long list of commercial and open-source gen AI models are now available, alongside new multimodal models that also understand images and other unstructured data. It’s already having a real-world impact. “It’s

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How Financial Services Should Prepare for Generative AI

Snowflake

In this blog post, we’ll walk through what the actual capabilities of LLMs are, and the steps financial organizations should take to harness this technology effectively and safely. But what might this transformation look like, from practical applications to potential risks? How do Gen AI/LLMs “democratize” access to insights?

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Data Warehouse vs Big Data

Knowledge Hut

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Technologies like Hadoop, Spark, Hive, Cassandra, etc.

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Educating ChatGPT on Data Lakehouse

Cloudera

I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics. ChatGPT is an excellent resource for gaining high-level insights and building awareness of any technology. I thought this was a fairly comprehensive list.