What is Unified Namespace and how it simplifies your Data Management Strategy

Explore the transformative potential of Unified Namespace (UNS) and how to simplify your data management strategy.
Discover how the Crosser platform seamlessly integrates with UNS, offering streamlined data management, dynamic data processing, enhanced data utilization, and seamless integration with various systems. From robust data collection to compliance assurance with Crosser data observability, data validation and historical data analysis.
Key Characteristics of Unified Namespace:
Standardized Data Model
A unified namespace means that you have a standardized data model defined, applied over a large variety of different systems and locations. This model harmonizes data from different systems into a well-defined structure.
Presentation Layer
The presentation layer is a centralized data hub where data producers can publish data and consumers can access it based on the standardized data model.
Inclusion of Historical Data
There is debate about whether the presentation layer should present only the current state of a system or if it should also incorporate historical data. This impacts the type of presentation layers that can be used.
Benefits of Using Unified Namespace
Simplifying Application Implementation
A standardized data model allows applications to use the data without needing to know about individual data sources. Different applications don't need to know anything about individual sources of data; they can just work and use the data based on this standardized model and don't need to care about where data originated and the format it had at the original source.
Easier Introduction of New Data Sources
New data sources can be easily integrated as soon as you have adapted the data they produce according to the model. It can be published into the presentation layer and be made available for any application that uses it. Existing applications can immediately use data from new producers without changes since they all follow this standardized data model.
Architectural Decoupling
Producers and consumers of data are decoupled, allowing for easy addition of new data sources and applications without affecting existing ones. This type of architecture simplifies working with data over time, as you will typically add both more applications and more data sources. This type of decoupling is a very useful design pattern for many cases.
Challenges in Defining a Data Model
Defining a data model across a large number of systems is challenging and often requires an iterative process. This is the main hurdle to get a unified namespace in place. Once defined, the rest is pretty straightforward, but it has to evolve over time. Starting with some systems and use cases, you can use that input for your design and then let it evolve over time.
Components of Data Modeling
Naming Conventions
Consistent naming conventions are necessary to harmonize data from different sources. For example, temperature is called temperature wherever it's coming from.
Contextual Information
Adding contextual information makes data more useful for applications. In addition to the actual values, you might add metadata such as the unit and the range of values expected from a sensor.
Hierarchical Structure
A hierarchical structure, often based on the ISA95 hierarchy, organizes data points and their relations. You can also express relations horizontally across different hierarchies, such as finding data from similar devices or energy measurements from all machines.
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