Overview
SCALE.sdm revolutionizes virtual product development by simplifying the management of vast data volumes and intricate workflows. Built for efficiency, scalability, and clarity, it supports teams from digital prototyping to production readiness. By centralizing simulation and test data, SCALE.sdm eliminates fragmentation and inefficiencies, enabling seamless collaboration and faster, data-driven decision-making through integrated analysis and standardized reports. Key features include seamless collaboration across teams and partners, ensuring efficient data sharing and eliminating fragmentation. Its data analysis and reporting tools transform simulation and test data into actionable insights, enabling smarter, faster decisions. SCALE.sdm also supports easy integration of ML and AI add-ons, accelerating development and enhancing precision. Additionally, SCALE.sdm provides a framework for systems engineering, ensuring transparent documentation, clear goals, and real-time progress tracking throughout the product development lifecycle. Its flexible, open platform integrates smoothly with existing tools and offers customization to meet your specific needs, giving you full control over the entire development cycle. Elevate your processes with SCALE.sdm and move confidently from digital prototypes to production-ready products.
Highlights
- Seamless Collaboration for Unified Teams: SCALE.sdm centralizes simulation and test data, fostering efficient collaboration across internal teams and external partners. With intuitive dashboards, real-time monitoring, and transparent access controls, it minimizes time spent searching for data, ensures project oversight, and protects valuable knowledge in a secure and connected environment.
- Advanced Analysis and Reporting for Smarter Decisions: Integrate and visualize simulation and test data with predefined, standardized reports to enable fast, accurate, and reproducible evaluations. SCALE.sdm empowers teams with robust analysis tools, actionable insights, and compliance monitoring, ensuring data-driven decisions and enhanced project outcomes.
- AI and ML Integration for Accelerated Development: Simplify the incorporation of AI and Machine Learning into your workflows with SCALE.sdm. Leverage advanced analysis tools for predictive insights, experimental design evaluation, and cause-and-effect analysis, reducing development time and boosting precision while adapting to domain-specific challenges.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Vendor refund policy
All sales are final and no refund will be issued.
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
SCALE.sdm
- Amazon EKS
- Amazon EKS Anywhere
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
SCALE.sdm is a platform for simulation data and process management, deployed on AWS with EC2, RDS, EFS, EKS, and an Application Load Balancer.
Deployment: Requires basic knowledge of the command-line interfaces kubectl and helm. Deployment requirements must be met.
Installation (basic process): 1. Prepare a custom values file (custom-values.yaml) with your environment parameters. 2. Run the installation command. 3. The command waits up to 10 minutes for stabilization.
Installation (full guide): https://www.scale.eu/sdm-manual/current/technical-manual/installation.html?sdm_token=Eic4eSeeLoh8lohKieDi
Support: For technical assistance or troubleshooting, contact support@scale.eu
Additional details
Usage instructions
SCALE.sdm is a platform for simulation data and process management deployed on AWS using EC2, RDS, EFS, EKS, and an Application Load Balancer. This section describes deployment, configuration, security, monitoring, and removal.
The application is deployed on Amazon EKS using Helm charts. Configuration is performed using a values.yaml file. In production environments, customers should pin container image tags, configure CPU and memory resources, define liveness and readiness probes, and enable exactly one exposure method (Ingress or Gateway API).
Regionalization of container images: All container images are hosted in region-specific Amazon ECR repositories. During deployment, images are pulled automatically from the AWS region where the EKS cluster is running. Cross-region image pulls are not required. If images are pulled from a private registry, imagePullSecrets must be configured.
Sensitive data location: Customer data including metadata, configuration, and logs is stored in Amazon RDS and Amazon EFS. No sensitive data is stored on EC2 instances. Database credentials and other secrets are managed using AWS Secrets Manager.
Encryption and cryptography: Amazon RDS and Amazon EFS are encrypted at rest using AWS KMS. All communication between components is protected using TLS. No customer-managed cryptographic keys are required. Key rotation is handled automatically by AWS.
Backup and recovery: Enable automated backups or snapshots for Amazon RDS. Amazon EFS can be backed up using AWS Backup. Recovery is performed by restoring from RDS snapshots or AWS Backup recovery points.
Deployment step: Bootstrap AWS CDK with appropriate IAM permissions.
Prepare a custom values.yaml file to configure replicaCount, image repository and tag, imagePullSecrets, service accounts, security contexts, networking, resources, autoscaling, and storage.
Install the application using helm upgrade --install with the custom values file and target namespace. Repeat for additional SCALE.sdm components if required.
Wait approx. 10 minutes for all pods and services to become ready.
Helm chart configuration overview: replicaCount controls the number of pod replicas. The image section defines repository, tag, and pull policy; fixed tags are recommended in production. imagePullSecrets are required for private registries. nameOverride and fullnameOverride allow explicit resource naming. serviceAccount controls IAM integration and token usage. podAnnotations and podLabels add metadata for monitoring and policies. podSecurityContext and securityContext define pod- and container-level hardening such as non-root execution. service defines internal or external exposure. ingress or httpRoute (Gateway API) exposes HTTP(S) endpoints and requires a compatible controller. resources define CPU and memory requests and limits. livenessProbe and readinessProbe configure health checks. autoscaling enables HPA and requires metrics-server. volumes and volumeMounts attach Secrets, ConfigMaps, or persistent volumes. nodeSelector, tolerations, and affinity control pod placement.
Using a custom values.yaml file is recommended. Multiple values files may be combined, with later files overriding earlier ones. Individual parameters can also be overridden using --set, including array values with zero-based indexes.
Uninstallation: Scale down operator workloads if applicable. Uninstall the Helm release using helm uninstall. Optionally delete the Kubernetes namespace.
Health monitoring and pricing: Monitor EC2, RDS, and EFS using the AWS Console. Pricing depends on instance sizes, storage consumption, data transfer, and backups.
Support and documentation: Technical support is available at support@scale.eu . Detailed installation documentation is available at https://www.scale.eu/sdm-manual/current/technical-manual/installation.html?sdm_token=Eic4eSeeLoh8lohKieDi
Support
Vendor support
support@scale.eu ; +49 841 12943-50
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.