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Data management and governance using AWS data mesh design AWS Lake Formation for data stewards, data custodians, and data managers to develop data management policies and enforce governance AWS Glue Data Catalog to register the metadata catalog and searchability AWS Glue Schema Registry to discover and control data stream schemas Data ingestion AWS Kinesis for streaming data sources AWS SQS for asynchronous batch data AWS Data Pipeline to build ETL Pipelines AWS Data Migration Service to migrate databases Data storage AWS S3 for blob data for any file type or schema/unstructured data AWS RedShift Spectrum Oracle SQL alternative structured/relational AWS RDS for managed or self-hosted SQL structured/relational AWS Aurora for managed MySQL, PostgreSQL for open-source structured/relational database alternatives AWS DynamoDB for key/value pair managed NoSQL (Not Only SQL) AWS EC2 for virtual machine-based self-hosted databases Data enrichment AWS Glue Studio GUI Apache Spark ETL Jobs AWS Glue ETL to read and write metadata to the Glue Data Catalog AWS Glue DataBrew to visually create, run, and monitor ETL jobs Data processing and analytics AWS Glue for data engineers and data managers to manage data sources AWS Athena for data engineers to use SQL on S3 AWS SageMaker Data Wrangler for data scientists supporting data-readiness and data maturity requirements AWS Databricks for managed collaborative workspace for data scientists Data serving AWS RStudio SageMaker for data scientists conducting statistical analysis AWS Quicksight (data engineer/business analyst) AWS EMR Studio (data analyst) AWS Kinesis Video Streams (OSINT analyst) Data mesh Microsoft Azure services Microsoft Azure data mesh design pattern starts with the adoption of Cloud-scale analytics, a framework to deploy reference architectures