This Guidance demonstrates how to combine and consolidate SAP and non-SAP data from disparate sources using AWS Datalakes and Machine Learning services allowing customers to unlock hidden business insights.
Configure OData service for extraction from SAP system (such as a sales order).
Create the OData connection from Amazon AppFlow to the SAP server. This can be over AWS PrivateLink for SAP on AWS/connect with AWS through VPN/ AWS Direct Connect, or over the internet.
In Amazon AppFlow, create the flow using the OData connection created in Step two to extract data from SAP and save to Amazon Simple Storage Service (Amazon S3) bucket.
Use AWS Glue DataBrew to cleanse the unnecessary data fields, integrate with the other data, then save the transformed data into another Amazon S3 bucket.
Use AWS Glue crawler to create the data catalog or metadata for the extracted data in an Amazon S3 bucket.
Use Amazon Athena or Amazon Redshift Spectrum to directly query the data from an Amazon S3 bucket.
Create dashboards to visualize the business data according to business requirements.
The data in the data lake can be used with AI/ML services like Amazon SageMaker, Amazon Forecast, and Amazon Personalize to make predictions and recommendations based on user data.
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.
The guidance for data lake with SAP data on AWS can be fully deployed with code. You can incorporate this automation to your own development pipeline to enable iteration and consistent deployments across your SAP landscape. Observability is derived from the managed services used for extraction and transformation. Logs and dashboards are available from CloudWatch.
The serverless components in the architecture are protected with AWS Identity and Access Management (IAM) for secure validation of user identity. The managed services only have access to the data that is specified. Access to the SAP workload is through Amazon AppFlow. Data is encrypted in transit and at rest. For audit logging, CloudTrail can be used to log the API calls with the various services used for the data lake.
All the serverless components are highly available. All non-SAP components automatically scale. Amazon AppFlow can move large volumes of data without breaking it down into multiple batches to increase reliability. Amazon S3 offers industry-leading scalability, data availability, security, and performance for your data lake.
By leveraging serverless technologies, you only provision the exact resources you use. Using Amazon S3 as the data lake optimizes the storage of the architecture with transformation of the data performed in AWS Glue DataBrew. For improved performance and agility, configure multiple flows in Amazon AppFlow for different groups of business data.
By utilizing serverless technologies, you only pay for the resources you use. To further optimize cost, make sure you are extracting only the business data groups that you need. To further optimize cost, extract only the business data groups that you need and minimize the number of flows being executed based on the granularity of your reporting needs. Organization of old or unwanted data can also be setup through Amazon S3 data tiering or deleting the data.
By utilizing managed services and dynamic scaling, we minimize the environmental impact of the backend services. As new options become available for Amazon AppFlow, make sure these are adopted to further optimize the volume and frequency of extraction. Reducing the quantity and frequency of extraction will improve sustainability as well as help reduce cost and improve performance.
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