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A data-centric software framework for distributing and managing real-time data in intelligent distributed systems.
Typical distributed systems require data to be shared across multiple devices and multiple networks, from the edge to the cloud. This is challenging because the sheer volume and variety of data — not to mention the stringent safety and security requirements — can easily overwhelm a network. These challenges require new ways to manage increased data volume, data variety, performance requirements, safety risk and security certifications. One of the most important innovations is the databus and its unique ability to manage data flow.
A databus is a data-centric software framework for distributing and managing real-time data in intelligent distributed systems. It allows applications and devices to work together as one, cohesive system.
In intelligent distributed systems, managing dataflow is critically important. The databus — designed specifically to manage dataflow in intelligent distributed systems — simplifies application and integration logic with a powerful data-centric paradigm. Instead of exchanging messages, software components communicate via shared data objects. Applications directly read and write the value of these objects, which are cached in each participant.
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The databus provides for data in motion where a database provides for data at rest.
A database implements data-centric storage. It saves old information that you can later search by relating properties of the stored data.
A databus implements data-centric interaction. It manages future information by letting you filter by properties of the incoming data. Data centricity can be defined by these properties:
It is important to note that a databus is not just a database that you interact with via a pub-sub interface. There is no database. A database implies storage: the data physically resides somewhere. A databus implements a purely virtual concept called a "global data space" and implies data in motion.
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Both database and databus technologies replace the application-application interaction with application-data-application interaction. This change is absolutely critical. It decouples applications and greatly eases scaling, interoperability and system integration which is crucial for intelligent distributed systems. The difference is really one of old data stored in a (likely centralized) database versus future data sent directly to the applications from a distributed databus.
When building an intelligent distributed system, try the Connectivity Standard Selection Tool to help decide which connectivity standard is the best fit for the use case.
The DDS standard has been used in thousands of systems to solve increasingly complex design integration challenges without the need for custom coding. In intelligent distributed systems, a common architecture pattern emerges that is made up of multiple databuses layered by communication QoS and data model needs. Engineers can create multiple (even hundreds) of DDS-based layers (databuses) to separate, isolate and selectively share communications.
A layered databus architecture is the ideal framework for resolving cross-network data sharing challenges and developing multi-tiered distributed systems of systems.
RTI Connext, built on the DDS standard, features a databus that eliminates network bottlenecks by allowing applications to exchange data via a publish-subscribe, trusted peer-to-peer communication method. Connext handles the details of data distribution, synchronization and management, including serialization and lifecycle management. Its reliability, security, performance and scalability are proven in the most demanding industrial systems.