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AWS Solutions Library

Guidance for Connected Customer Journey Hub on AWS

Overview

This Guidance helps to create a single source of truth of customer touch points to automatically understand and extract customer linked information from siloed, raw, and disparate data.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

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.

Telecoms data is ingested using data pipelines built using AWS Cloud Development Kit (AWS CDK), AWS CloudFormation, and serverless application model (SAM). Continuous integration/continuous delivery (CI/CD) toolsets such as AWS CodePipeline are used to orchestrate deployment and promote code through environments. AWS Glue Studio, AWS Step Functions, and AWS Glue DataBrew are used to provide orchestration of the data operations lifecycle. SageMaker pipelines are used to orchestrate the ML lifecycle.

Read the Operational Excellence whitepaper 

All data is encrypted both in motion and at rest. Encrypted Amazon S3 buckets store data. Neptune database is also encrypted and is secured in a private subnet within the VPC. SageMaker can only access that data via the VPC and not via the internet. Training is done in secure containers and the results are stored in encrypted S3 buckets. 

Read the Security whitepaper 

Neptune is deployed across multiple availability zones. SageMaker hosting is used to server the trained model, which takes advantage of multiple Availability Zones (AZs) and Elastics scaling groups. All other services are serverless, which means that they are inherently highly available across multiple AZs in a region.

Read the Reliability whitepaper 

Serverless technology is used where possible. In the case of Neptune, autoscaling is configured to deal with unpredictable read patterns. SageMaker endpoints can scale up and down as needed to ensure the minimum number of instances needed are running. 

Read the Performance Efficiency whitepaper 

Serverless services are used where possible, making sure that customers pay for only the resources consumed. Lambda power tuning is used to optimize cost while maintaining performance. Autoscaling is used in Neptune to automatically turn off read replicas when not being used. SageMaker endpoints can scale up and down as needed to ensure the minimum number of instances needed are running. Instance sizes are measured by using SageMaker Inference Recommender to make sure costs are minimized. 

Read the Cost Optimization whitepaper 

By extensively utilizing managed services and dynamic scaling, we minimize the environmental impact of the backend services. All compute instances are right-sized to provide maximum utility.

Read the Sustainability whitepaper 

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.

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