Remove Data Remove Metadata Remove Unstructured Data
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Agents of Change: Navigating 2025 with AI and Data Innovation

Data Engineering Weekly

In this post, we delve into predictions for 2025, focusing on the transformative role of AI agents, workforce dynamics, and data platforms. For professionals across domains—data engineers, AI engineers, and data scientists—the message is clear: adapt or become obsolete.

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Unapologetically Technical Episode 20 – Shane Murray

Jesse Anderson

I n this episode of Unapologetically Technical, I interview Shane Murray, Field CTO at Monte Carlo Data. Shane shares his compelling journey from studying math and finance in Sydney, Australia, to leading AI strategy at a major data observability company in New York.

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Your Enterprise Data Needs an Agent

Snowflake

Agents need to access an organization's ever-growing structured and unstructured data to be effective and reliable. As data connections expand, managing access controls and efficiently retrieving accurate informationwhile maintaining strict privacy protocolsbecomes increasingly complex. text, audio) and structured (e.g.,

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Bring Order To The Chaos Of Your Unstructured Data Assets With Unstruk

Data Engineering Podcast

Summary Working with unstructured data has typically been a motivation for a data lake. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. No more scripts, just SQL.

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Build Better Data Pipelines with SQL and Python in Snowflake

Snowflake

Data transformations are the engine room of modern data operations — powering innovations in AI, analytics and applications. As the core building blocks of any effective data strategy, these transformations are crucial for constructing robust and scalable data pipelines. This puts data engineers in a critical position.

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AI and Data Predictions 2025: Strategies to Realize the Promise of AI

Snowflake

Together with a dozen experts and leaders at Snowflake, I have done exactly that, and today we debut the result: the “ Snowflake Data + AI Predictions 2024 ” report. When you’re running a large language model, you need observability into how the model may change as it ingests new data. The next evolution in data is making it AI ready.

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Scale Unstructured Text Analytics with Batch LLM Inference

Snowflake

Large language models (LLMs) are transforming how we extract value from this data by running tasks from categorization to summarization and more. While AI has proved that real-time conversations in natural language are possible with LLMs, extracting insights from millions of unstructured data records using these LLMs can be a game changer.