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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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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. The table is registered in AWS Glue Data Catalog.

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Use Amazon OpenSearch Ingestion to migrate to Amazon OpenSearch Serverless

AWS Big Data

Migration of metadata such as security roles and dashboard objects will be covered in another subsequent post. For index , you can leave it as default, which will get the metadata from the source index and write to the same name in the destination as of the sources.

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Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

For example, you can use metadata about the Kinesis data stream name to index by data stream ( ${getMetadata("kinesis_stream_name") ), or you can use document fields to index data depending on the CloudWatch log group or other document data ( ${path/to/field/in/document} ).

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Iceberg tables maintain metadata to abstract large collections of files, providing data management features including time travel, rollback, data compaction, and full schema evolution, reducing management overhead. Snowflake writes Iceberg tables to Amazon S3 and updates metadata automatically with every transaction.

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

Organizations have multiple Hive data warehouses across EMR clusters, where the metadata gets generated. The onboarding of producers is facilitated by sharing metadata, whereas the onboarding of consumers is based on granting permission to access this metadata. compute.internal ). Choose Submit job run.

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Use AWS Glue Data Catalog views to analyze data

AWS Big Data

The objective is to create views in the Data Catalog so you can create a single common view schema and metadata object to use across engines (in this case, Athena). Solution overview For this post, we use the Women’s E-Commerce Clothing Review. Doing so lets you use the same views across your data lakes to fit your use case.