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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

AWS Big Data

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation.

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5 Benefits intelligent document processing brings to content management

CIO Business Intelligence

As explained in a previous post , with the advent of AI-based tools and intelligent document processing (IDP) systems, ECM tools can now go further by automating many processes that were once completely manual. That relieves users from having to fill out such fields themselves to classify documents, which they often don’t do well, if at all.

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Enhance data governance with enforced metadata rules in Amazon DataZone

AWS Big Data

We’re excited to announce a new feature in Amazon DataZone that offers enhanced metadata governance for your subscription approval process. With this update, domain owners can define and enforce metadata requirements for data consumers when they request access to data assets. Key benefits The feature benefits multiple stakeholders.

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RAG Powered Document QnA & Semantic Caching with Gemini Pro

Analytics Vidhya

Introduction With the advent of RAG (Retrieval Augmented Generation) and Large Language Models (LLMs), knowledge-intensive tasks like Document Question Answering, have become a lot more efficient and robust without the immediate need to fine-tune a cost-expensive LLM to solve downstream tasks.

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Why Modern Data Challenges Require a New Approach to Governance

By capturing metadata and documentation in the flow of normal work, the data.world Data Catalog fuels reproducibility and reuse, enabling inclusivity, crowdsourcing, exploration, access, iterative workflow, and peer review. It adapts the deeply proven best practices of Agile and Open software development to data and analytics.

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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Writing SQL queries requires not just remembering the SQL syntax rules, but also knowledge of the tables metadata, which is data about table schemas, relationships among the tables, and possible column values. Although LLMs can generate syntactically correct SQL queries, they still need the table metadata for writing accurate SQL query.

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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In this blog post, we’ll discuss how the metadata layer of Apache Iceberg can be used to make data lakes more efficient. You will learn about an open-source solution that can collect important metrics from the Iceberg metadata layer. This ensures that each change is tracked and reversible, enhancing data governance and auditability.

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