Secure and controlled data exchange between organizations has become a strategic necessity in the digital economy. Data spaces enable information sharing among different entities while maintaining sovereignty and regulatory compliance. In this context, Eclipse Dataspace Components (EDC) emerge as a reference implementation based on International Data Spaces Association (IDSA) standards. By deploying EDC on Amazon Web Services (AWS), companies can combine the flexibility of the Eclipse ecosystem with the scalability and security of the cloud, building robust infrastructures for interoperable data sharing.
The architecture of a data space relies on standardized protocols such as the Dataspace Protocol (DSP), which defines how participants — data providers and consumers — discover each other, negotiate contracts, and transfer assets. Additionally, the Decentralized Claims Protocol (DCP) adds a layer of decentralized trust: each participant holds a Decentralized Identifier (DID) and stores Verifiable Credentials (VCs) in their identity hub, enabling authentication without depending on a central authority. This approach perfectly aligns with modern cybersecurity principles, where identity and access management must be resilient and auditable.
The core of the EDC ecosystem is the connector, composed of a control plane that handles policy and contract negotiation, and a data plane that executes information transfer between organizations. This separation allows each plane to scale independently and enforce granular security policies. To customize the connector and integrate it with native AWS services — such as Amazon S3 for asset storage, AWS Secrets Manager for credential management, or Amazon DynamoDB as a metadata store — you need to build a custom version of the connector using Gradle. The process involves three steps: defining a version catalog in gradle/libs.versions.toml, creating launcher modules for both control and data planes, and registering all submodules in the project settings file. This results in a lightweight connector tailored to specific deployment needs.
Deploying a data space on AWS offers clear advantages: elastic provisioning, high availability, and a managed services ecosystem that reduces operational overhead. For example, using Amazon ECS to orchestrate connector containers, Amazon RDS Aurora as a relational database for metadata, and Amazon API Gateway as the entry point for catalog and negotiation APIs yields a production-ready architecture. Furthermore, integration with artificial intelligence (AI) services enriches shared data with predictive analytics or automated classification, while AI agents can orchestrate complex workflows within the data space.
At this point, having an expert technology partner makes a difference. Q2BSTUDIO is a software development and technology company that masters custom application development, cloud solutions on AWS and Azure, artificial intelligence integration, and cybersecurity consulting. Their team has deep knowledge of Eclipse Dataspace Components and can guide organizations from conceptual design to production deployment, ensuring every piece — from the federated catalog to the identity hub — aligns with business goals. Additionally, Q2BSTUDIO offers Business Intelligence (BI) services with Power BI, enabling visualization and analysis of shared data within the space, and develops AI agents that automate pattern detection and decision-making.
For companies looking to start their data space journey, the combination of EDC and AWS is a solid path. IDSA standards and the DSP protocol guarantee interoperability, while the cloud provides the technical foundation to scale. However, connector customization and AWS service integration require specialized expertise. This is where Q2BSTUDIO's cloud services for AWS and Azure add value, offering everything from building the custom connector to orchestrating multicloud environments. Likewise, adopting artificial intelligence within the data space can accelerate knowledge extraction: for example, AI models that automatically classify data assets by sensitivity, or intelligent agents that autonomously negotiate contracts based on predefined policies. Q2BSTUDIO's AI solutions are designed to integrate seamlessly into distributed data architectures, enhancing automation and business intelligence.
In summary, Eclipse Dataspace Components on AWS represent a real opportunity for organizations to share data securely, scalably, and in compliance with international standards. With the right support — such as that provided by Q2BSTUDIO — it is possible to overcome technical complexity and focus on business value: from custom applications that manage data flows between partners to BI dashboards that monitor data space performance in real time. Cybersecurity, artificial intelligence, and the cloud are not isolated concepts; they converge in this architecture to enable a new paradigm of business collaboration. We invite companies to explore these capabilities and contact experts who can accompany them in implementing their own data space on AWS.
