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This Guidance helps credit unions build a modern member engagement data analytics pipeline. Credit unions have disparate sources of data, making it difficult for them to derive useful insights. This architecture helps credit unions bring data together in a single location and create visualizations to better understand member behavior and needs. Insights from these visualizations will help credit unions increase engagement through a quality digital experience, especially for those members who prefer to interact virtually rather than visiting in person.
Please note: [Disclaimer]
Architecture Diagram
Step 1
Member data flows from core baking databases into AWS Database Migration Service (AWS DMS).
Step 2
Using Amazon Simple Storage Service (Amazon S3), AWS Lake Formation helps build a scalable data lake for credit unions.
Step 3
Lake Formation enables unified governance to centrally manage security, access control, and audit trails. This helps ensure financial regulatory compliance and enables automatic schema discovery and conversion to a required format.
Step 4
AWS Glue extracts, transforms, catalogs, and ingests data across multiple data stores. AWS Glue DataBrew handles visual data preparation, such as member insights. AWS Lambda enriches and validates data.
Step 5
Amazon QuickSight provides machine learning (ML)-powered business intelligence, such as member dashboards. AWS ML services build, train, and deploy ML models and add intelligence to applications.
Amazon Athena provides interactive querying, analyzing, and processing capabilities. Amazon S3 stores trained models from Amazon SageMaker and queries from Athena.
Step 6
Unified member profile information is stored in Amazon OpenSearch Service.
Step 7
AWS Glue discovers, prepares, and integrates identity resolution data from Amazon Aurora and builds a single member profile view with SageMaker.
Step 8
Amazon API Gateway provides APIs as microservices.
Step 9
Send the unified member data for activation. This data can help deliver personalized experiences and campaigns, allowing for deeper engagement with members.
Step 10
Enrich the data sources by exporting to AWS DMS, AWS AppFlow, or AWS Data Sync, based on the destination type.
Step 1
Member data flows from core baking databases into AWS Database Migration Service (AWS DMS).
Step 2
Using Amazon Simple Storage Service (Amazon S3), AWS Lake Formation helps build a scalable data lake for credit unions.
Step 3
Lake Formation enables unified governance to centrally manage security, access control, and audit trails. This helps ensure financial regulatory compliance and enables automatic schema discovery and conversion to a required format.
Step 4
AWS Glue extracts, transforms, catalogs, and ingests data across multiple data stores. AWS Glue DataBrew handles visual data preparation, such as member insights. AWS Lambda enriches and validates data.
Step 5
Amazon QuickSight provides machine learning (ML)-powered business intelligence, such as member dashboards. AWS ML services build, train, and deploy ML models and add intelligence to applications.
Amazon Athena provides interactive querying, analyzing, and processing capabilities. Amazon S3 stores trained models from Amazon SageMaker and queries from Athena.
Step 6
Unified member profile information is stored in Amazon OpenSearch Service.
Step 7
AWS Glue discovers, prepares, and integrates identity resolution data from Amazon Aurora and builds a single member profile view with SageMaker.
Step 8
Amazon API Gateway provides APIs as microservices.
Step 9
Send the unified member data for activation. This data can help deliver personalized experiences and campaigns, allowing for deeper engagement with members.
Step 10
Enrich the data sources by exporting to AWS DMS, AWS AppFlow, or AWS Data Sync, based on the destination type.
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
Use Amazon CloudWatch for application and infrastructure monitoring. This Guidance can be deployed using infrastructure as code within AWS CloudFormation, allowing for automation for fast iteration and consistent deployments.
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SecurityRead the Security whitepaper
Lake Formation is used for unified governance to centrally manage access control at the table-, row-, and column-security level. API Gateway enforces policies that control security aspects such as authentication, authorization, or traffic management.
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ReliabilityRead the Reliability whitepaper
The serverless architecture enables this Guidance to be automatically scalable, highly available, and deployed across all Availability Zones. Services like Lambda, QuickSight, AWS Glue, Athena, and Amazon S3 are managed services that span multiple Availability Zones within a Region to allow for built-in resiliency.
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Performance EfficiencyRead the Performance Efficiency whitepaper
By using serverless technologies, you provision only the exact resources you need. To maximize the performance of this Guidance, test it with multiple instance types. For example, you can use Amazon API Gateway Edge endpoints for geographically dispersed customers. An edge-optimized API endpoint is best for geographically distributed clients. API requests are routed to the nearest Amazon CloudFront point of presence for regional customers (and when using other AWS services within the same Region).
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Cost OptimizationRead the Cost Optimization whitepaper
By using serverless technologies that automatically scale, such as Amazon S3, Lambda, Athena, DataSync, and Amazon AppFlow, you pay only for the resources you use. Serverless services do not incur costs while they’re idle.
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SustainabilityRead the Sustainability whitepaper
The data lake automatically moves infrequently accessed data to cold storage with Amazon S3 Lifecycle configurations. By extensively using managed services and dynamic scaling, this architecture minimizes the environmental impact of the backend services.
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.
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.