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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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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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Expand data access through Apache Iceberg using Delta Lake UniForm on AWS

AWS Big Data

Under the hood, UniForm generates Iceberg metadata files (including metadata and manifest files) that are required for Iceberg clients to access the underlying data files in Delta Lake tables. Both Delta Lake and Iceberg metadata files reference the same data files. in Delta Lake public document. Appendix 1.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Central to a transactional data lake are open table formats (OTFs) such as Apache Hudi , Apache Iceberg , and Delta Lake , which act as a metadata layer over columnar formats. For more examples and references to other posts, refer to the following GitHub repository. This post is one of multiple posts about XTable on AWS.

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Metadata is Like Packaging: Seeing Beyond the Library Card Metaphor

Ontotext

way we package information has a lot to do with metadata. The somewhat conventional metaphor about metadata is the one of the library card. This metaphor has it that books are the data and library cards are the metadata helping us find what we need, want to know more about or even what we don’t know we were looking for.

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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Q generative SQL for Amazon Redshift uses generative AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly within Amazon Redshift, accelerating the query authoring process for users and reducing the time required to derive actionable data insights.

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RDF-Star: Metadata Complexity Simplified

Ontotext

This is a graph of millions of edges and vertices – in enterprise data management terms it is a giant piece of master/reference data. Not Every Graph is a Knowledge Graph: Schemas and Semantic Metadata Matter. open-world vs. closed-world assumptions). They aren’t concerned with publishing or integrating data.

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