What is a Unified Data Model and Why Would You Use It

Managing modern application environments is hard. A unified data model can make it easier. Here’s how.

The nature of modern app environments

Modern distributed application systems are growing increasingly complex. Not only are they larger and spread across scale-out environments, but they are also composed of more layers, due especially to the trend toward software-defined networking, storage and everything else. The environments are also highly dynamic, with configurations that are auto-updated on a recurring basis.

Add to this picture microservices architectures and hybrid clouds, and things get even more complex.

Whereas in the past you would typically have run a monolithic application in a static environment on a single server, today you probably have containerized microservices distributed across clusters of servers, using software-defined networks and storage layers. Even if you have simpler virtual machines, your infrastructure is still likely to be highly distributed, and your machine images might move between host servers.

This complexity makes it difficult to map, manage and integrate multiple tools within your environment, especially when each tool uses its own data model. It creates multiple issues for DevOps practitioners and developers alike.

What is a Unified Data Model?

This is why organizations are increasingly adopting unified data models. A unified data model creates an opportunity for an organization to analyze data from multiple sources in the context of shared business initiatives. 

A unified data model forces your DevOps and development teams to determine the methods, practices, and architectural patterns that correlate to the best outcomes in your organization. It will also force your institution to future-proof your data architecture by leveraging new technology data types and attributes.

As the complexity of systems increases, diminishing returns of different data modeling impact our ability to maintain and monitor web applications. Individual modeling for different systems creates a contextual gap in regard to the overarching infrastructure. CA Technologies’ white paper on the Essentials of Root Cause Analytics goes into much greater detail on this.

A unified data model acts as a bridge between your different ecosystems, allowing you to contextualize data sources across multiple services. It acts as a foundation upon which data can be consistently consumed, combined and correlated, which allows for machine learning application across different data sets. It could be argued that this is the future of DevOps monitoring and maintenance.

Lastly, a unified data model will allow for refactoring and migration of data across your infrastructure. As a result, careful consideration should be given to the flexibility of the data components in your organization’s ecosystem, and design should be addressed with future-proofing in mind. Every data layer and every data source should serve to increase the understanding of your overarching data model and ecosystem.