AWS solutions documentation change
Summary
Restructured architecture descriptions with bold titles ('AWS Serverless Data Lake Framework' and 'Multi-Source Analytics Lakehouse') for better readability
Security assessment
Formatting changes only. Security elements like Lake Formation governance and access controls remain unchanged. No new security content added.
Diff
diff --git a/solutions/data-lakes-on-aws/index.md b/solutions/data-lakes-on-aws/index.md index e7715988f..4b943a6c8 100644 --- a//solutions/data-lakes-on-aws/index.md +++ b//solutions/data-lakes-on-aws/index.md @@ -14 +14,3 @@ Accelerate data-driven decisions -Architecture 1:Deploy a serverless data lake framework that transforms raw data into actionable insights using AWS analytics services. Enable your business analysts to query processed data through Amazon Athena while maintaining comprehensive data governance through AWS Lake Formation. Architecture 2:Deploy a unified data lakehouse architecture that seamlessly ingests data from diverse sources including streaming inventory, relational databases, and enterprise systems like SAP. Reduce development time by leveraging zero-ETL capabilities and open table formats that eliminate complex data pipeline engineering. +**AWS Serverless Data Lake Framework:** Deploy a serverless data lake framework that transforms raw data into actionable insights using AWS analytics services. Enable your business analysts to query processed data through Amazon Athena while maintaining comprehensive data governance through AWS Lake Formation. + +**Multi-Source Analytics Lakehouse with AI-Powered Insights:** Deploy a unified data lakehouse architecture that seamlessly ingests data from diverse sources including streaming inventory, relational databases, and enterprise systems like SAP. Reduce development time by leveraging zero-ETL capabilities and open table formats that eliminate complex data pipeline engineering. @@ -18 +20,3 @@ Streamline data processing workflows -Architecture 1:EImplement automated, event-driven data pipelines that efficiently transform and catalog your data across its lifecycle. AWS Step Functions orchestrates the workflow while AWS Glue handles ETL processes, converting data to optimized formats for improved query performance. Architecture 2:Enable business teams to generate actionable insights through natural language queries with Amazon Bedrock and visualize results with Amazon QuickSight. Query data through existing spark platform or leveraging the AWS services under one unified platform. Marketing and sales teams can independently access the data they need while IT maintains centralized governance through AWS Lake Formation. +**AWS Serverless Data Lake Framework:** Implement automated, event-driven data pipelines that efficiently transform and catalog your data across its lifecycle. AWS Step Functions orchestrates the workflow while AWS Glue handles ETL processes, converting data to optimized formats for improved query performance. + +**Multi-Source Analytics Lakehouse with AI-Powered Insights:** Enable business teams to generate actionable insights through natural language queries with Amazon Bedrock and visualize results with Amazon QuickSight. Query data through existing spark platform or leveraging the AWS services under one unified platform. Marketing and sales teams can independently access the data they need while IT maintains centralized governance through AWS Lake Formation. @@ -22 +26,3 @@ Enhance data accessibility securely -Architecture 1:Create a unified data environment where teams can access and analyze data through their preferred tools while maintaining centralized governance. AWS Lake Formation provides fine-grained access controls while Amazon SageMaker and Amazon Bedrock enable advanced analytics and AI-powered insights from your data lake. Architecture 2:Implement a secure, well-governed data environment using Lakehouse for Amazon SageMaker with federated catalogs and AWS Lake Formation controls. This architecture allows you to maintain data security and compliance while enabling cross-account data sharing between producer and consumer accounts. +**AWS Serverless Data Lake Framework:** Create a unified data environment where teams can access and analyze data through their preferred tools while maintaining centralized governance. AWS Lake Formation provides fine-grained access controls while Amazon SageMaker and Amazon Bedrock enable advanced analytics and AI-powered insights from your data lake. + +**Multi-Source Analytics Lakehouse with AI-Powered Insights:** Implement a secure, well-governed data environment using Lakehouse for Amazon SageMaker with federated catalogs and AWS Lake Formation controls. This architecture allows you to maintain data security and compliance while enabling cross-account data sharing between producer and consumer accounts.