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Discover And De-Clutter Your Unstructured Data With Aparavi

Data Engineering Podcast

Summary Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc.

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Top 10 Data Engineering & AI Trends for 2025

Monte Carlo

Small data is the future of AI (Tomasz) 7. The lines are blurring for analysts and data engineers (Barr) 8. Synthetic data matters—but it comes at a cost (Tomasz) 9. The unstructured data stack will emerge (Barr) 10. Data quality risks are evolving — but data quality management isn’t.

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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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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.

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Accelerate AI Development with Snowflake

Snowflake

Deliver multimodal analytics with familiar SQL syntax Database queries are the underlying force that runs the insights across organizations and powers data-driven experiences for users. Traditionally, SQL has been limited to structured data neatly organized in tables.

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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.

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Data Integrity for AI: What’s Old is New Again

Precisely

The demand for higher data velocity, faster access and analysis of data as its created and modified without waiting for slow, time-consuming bulk movement, became critical to business agility. The DW costs were skyrocketing, and it was nearly impossible to keep up with the scaling requirements.