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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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Jumia builds a next-generation data platform with metadata-driven specification frameworks

AWS Big Data

Jumia is a technology company born in 2012, present in 14 African countries, with its main headquarters in Lagos, Nigeria. Solution overview The basic concept of the modernization project is to create metadata-driven frameworks, which are reusable, scalable, and able to respond to the different phases of the modernization process.

Metadata 103
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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.

Metadata 101
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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.

Metadata 102
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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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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

Add this policy to the AWS Glue role and Amazon MWAA role: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject", "s3:PutObjectAcl" ], "Resource": "arn:aws:s3:::sample-inp-bucket-etl- /*" } ] } In Account B, create the IAM policy policy_for_roleB specifying Account A as a trusted entity.

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How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

This populates the technical metadata in the business data catalog for each data asset. The business metadata, can be added by business users to provide business context, tags, and data classification for the datasets. If new AWS Glue tables or metadata is created or updated, then it starts the data source sync job.

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