Backup of SAP on AWS Guidelines
Start course

In this course, we discuss planning for data recovery, including disaster recovery of SAP workloads in AWS. We present and discuss some of the design and best practices gathered by AWS customers, AWS Experts, and SAP Specialists running SAP workloads on AWS.

Learning Objectives

We introduce best practices for business continuity and disaster recovery related to SAP workloads on AWS. The recommendations are aligned with the Reliability pillar of the Well-Architected Framework and focus on planning for data protection and recovery of SAP solutions implemented using AWS services.

Intended Audience

This course is intended for SAP architects and SAP Operators who deploy and maintain SAP workloads on AWS. This course also aligns with the objectives of the AWS Certified: SAP on AWS Specialty (PAS-C01) exam.


To get the most from this course, you will need to meet the requirements for the AWS Solutions Architect Associate or AWS SysOps Associate certifications or the equivalent experience. This includes the function, anatomy, and operation of core AWS services that are relevant to SAP implementations, such as: 

  • The AWS global infrastructure, Amazon VPCs, Amazon EC2, EBS, EFS, S3, Glacier, IAM, CloudWatch, CloudTrail, the AWS CLI, Amazon Route 53
  • The Well-Architected Framework

It is also assumed that you are familiar with SAP software workloads and their implementation. SAP is well known for enterprise resource planning (ERP) applications, including SAP Business Suite, SAP Net weaver, SAP S/4HANA solutions, and supporting products.


Backup of SAP on AWS Guidelines. The well-architected framework documentation includes special sections where guidance is provided for specific use cases or implementations. These are called the AWS Well-Architected Framework Lens. The SAP Lens has been designed to assist in the implementation of native SAP workloads on AWS. It's not unusual for SAP workloads to represent mission-critical applications. It is important to understand SAP architectures and the restrictions that they impose. 

If we examine the best practices for reliability first and foremost, is to design for failure according to your business requirements. For SAP workloads on AWS, it is important to start with a definition of the availability expectations. You are expected for your availability to be defined and it's going to dictate the architecture pattern to be used as well as your backup, retention and recovery procedures. It's also important to differentiate between availability and disaster recovery. 

Availability relates to situations where a customer continues to access the SAP system despite a component failure. If the application being used becomes unavailable, a disaster recovery event is triggered. To define SAP workload availability expectations, it is critical that you, number one, identify the SAP applications and their dependencies. Number two, classify those systems based on impact to failure. Number three, assess the business impact of an outage for each of the systems. 

Number four, be aware of compliance and regulatory requirements for your business. And last but not least, define a minimum percentage uptime in terms of Recovery Time Objective, Recovery Point Objective, and Mean Time To Recovery. This is going to entail identifying the SAP applications and interdependencies. Then define their priority based on impact of failure. The expected best practice is to use an architecture that supports your availability and capacity needs, define Critical Data to the SAP workload and define its availability, and test to validate the results according to expectations.


About the Author
Jorge Negrón
AWS Content Architect
Learning Paths

Experienced in architecture and delivery of cloud-based solutions, the development, and delivery of technical training, defining requirements, use cases, and validating architectures for results. Excellent leadership, communication, and presentation skills with attention to details. Hands-on administration/development experience with the ability to mentor and train current & emerging technologies, (Cloud, ML, IoT, Microservices, Big Data & Analytics).