This Guidance helps consumer packaged goods (CPG) companies use a data mesh to break down data silos and scale their data analytics capabilities. In a data mesh, business domains maintain control of their own data and offer data as a product to the rest of the enterprise through a central data catalog. Data producers define who can use their data, and governance is managed at the central data platform. Data contracts define the data, and data is exposed to the enterprise through APIs. Data consumer domains can discover and subscribe to datasets through the central data platform.
Please note: [Disclaimer]
Architecture Diagram
Step 1
Collect data from multiple data sources and ingest this data into the different business domains across the enterprise.
Step 2
Producer domains maintain ownership and autonomy of the data for their domain. Each domain creates metadata catalogs using AWS Glue Data Catalog. The producer domain controls data permission.
Step 3
Datasets from producer domains are presented as a product to the central governance account, and producers create AWS Lake Formation data catalog entities.
Step 4
The central AWS Lake Formation data catalog shares the data catalog resources back to the producer account with required permissions through AWS Lake Formation resource links to metadata databases and tables.
Step 5
Data changes made within the producer account are automatically propagated into the central governance copy of the catalog.
Step 6
Amazon Neptune helps provide full context for the data through data lineage.
Step 7
Amazon DynamoDB stores immutable data contracts. Consumers can subscribe to specific data contract attributes. Amazon API Gateway and AWS Lambda expose data to the enterprise as APIs.
Step 8
Consumer domains can be the same as the producer domains or unique consumer domains. Amazon Athena and Amazon QuickSight query and visualize data. Amazon Athena, Amazon EMR, and Amazon Redshift perform advanced analytics. Amazon SageMaker helps build machine learning (ML) models.
Step 1
Collect data from multiple data sources and ingest this data into the different business domains across the enterprise.
Step 2
Producer domains maintain ownership and autonomy of the data for their domain. Each domain creates metadata catalogs using AWS Glue Data Catalog. The producer domain controls data permission.
Step 3
Datasets from producer domains are presented as a product to the central governance account, and producers create AWS Lake Formation data catalog entities.
Step 4
The central AWS Lake Formation data catalog shares the data catalog resources back to the producer account with required permissions through AWS Lake Formation resource links to metadata databases and tables.
Step 5
Data changes made within the producer account are automatically propagated into the central governance copy of the catalog.
Step 6
Amazon Neptune helps provide full context for the data through data lineage.
Step 7
Amazon DynamoDB stores immutable data contracts. Consumers can subscribe to specific data contract attributes. Amazon API Gateway and AWS Lambda expose data to the enterprise as APIs.
Step 8
Consumer domains can be the same as the producer domains or unique consumer domains. Amazon Athena and Amazon QuickSight query and visualize data. Amazon Athena, Amazon EMR, and Amazon Redshift perform advanced analytics. Amazon SageMaker helps build machine learning (ML) models.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
This architecture was designed with fully managed and serverless services for a highly available and scalable way to move data across the enterprise. You can automatically reflect changes made to data producers in the central data governance account.
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SecurityRead the Security whitepaper
Producer domains control access to the data products developed by their domain using AWS Identity and Access Management (IAM). Lake Formation and IAM use the central data governance account to enforce data access permissions to consumer domains.
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ReliabilityRead the Reliability whitepaper
This architecture consists of loosely-coupled components. Producer domains create data products and add them to a central data governance account. This is achieved without interrupting operations from consumer domains using other data products from that same governance account.
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Performance EfficiencyRead the Performance Efficiency whitepaper
Scalable and highly available services such as Amazon Simple Storage Service (Amazon S3), AWS Glue, API Gateway, and Lambda are used as core components to increase performance. Amazon S3 is designed to store any amount of data in any format. AWS Glue, API Gateway, and Lambda scale to accommodate both increases and decreases in traffic.
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Cost OptimizationRead the Cost Optimization whitepaper
This architecture is designed with a serverless-first approach, using services such as AWS Glue, Lambda, and Amazon S3 for cost efficiency. Lambda and AWS Glue crawlers in addition to extract, transform, and load (ETL) jobs are billed by time spent running, which helps to reduce cost while not in operation. Amazon S3 offers a lifecycle management feature that can move infrequently accessed data to lower cost storage tiers.
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SustainabilityRead the Sustainability whitepaper
This architecture uses serverless services, such as AWS Glue, API Gateway, and Lambda, which will provision the minimum amount of resources necessary to accomplish its intended operation. Lambda and API Gateway provision resources when handling API requests, then automatically shuts down when finished. Likewise, AWS Glue provisions resources while crawling data sources or running ETL jobs, then shuts down those resources once the job is finished.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.