Data virtualization
What is data virtualization?
Data virtualization is a modern data management approach that creates a unified data access layer, allowing users to view, access, and analyze data from multiple sources without needing to know the physical location or technical format of that data. Think of it as creating a virtual layer that sits between your applications and various data sources, providing a seamless way to work with data regardless of where it's stored.
Why is data virtualization important?
Data virtualization has become increasingly important in today's rapidly evolving technology landscape. It allows organizations to streamline data access and integration without the prolonged and often costly process of physical data consolidation.
Benefits of data virtualization
- Simplified data access: Users can access data without managing and maintaining the underlying technical complexities of multiple data sources.
- Real-time data integration: Data can be accessed in real time without the need to physically move or copy it between systems.
- Increased agility: Organizations can quickly adapt to changing data requirements without rebuilding entire data pipelines.
- Reduced IT dependency: Business users can access the data they need with less reliance on IT specialists.
- Faster insights: By streamlining data access, organizations can discover insights and make decisions more quickly.
How does data virtualization work?
Data virtualization simplifies data access by creating a layer between data sources and applications. It uses metadata to map operations and process queries efficiently. When a query is made, the system finds the right data sources, runs optimized sub-queries in parallel, and combines the results in real time without storing copies. A caching system helps speed up frequently used data.
Data virtualization is just one aspect of an effective legacy transformation program. As an example, as a part of Pega Blueprint™, Pega Live Data enables teams to abstract and integrate data from multiple sources, including legacy systems, without requiring direct modifications or complex migrations.
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Key components of data virtualization
Data sources layer
The foundational level where the system connects to and interacts with the various underlying data repositories
Abstraction layer
Allows data from many differing sources to be viewed as if it was from one single source
Virtualization engine
Responsible for orchestrating the access, integration, and delivery of virtualized data
Data integration services
Provides a unified view of data without physically moving it
Consumption layer
Serves as the point of access for users and applications to interact with the virtualized data
Security & governance framework
Ensures that the virtualized data is accessed and used responsibly and securely
Use cases for data virtualization
Supply chain management
Standardizing data from diverse sources for seamless collaboration between suppliers and manufacturers
Application development
Allowing developers to access data from multiple sources through a single interface, as Pega does with Data Pages
Virtual data marts
Creating unified views of data from various sources for specific departments or functions
Getting started with data virtualization
Data virtualization enables applications to operate independently of underlying data sources, boosting agility and efficiency. For faster optimization, especially in the context of a larger legacy transformation effort, an AI workflow builder like Pega Blueprint can help organizations visualize and quickly integrate new data workflows, without having to re-architect disparate systems.
