Datasheet: Data Virtualization

Data Virtualization by Composite Software

Avoid the burdens of physical data consolidation and accelerate business initiatives,

for less

 

Why Data Virtualization is Important
The virtualization revolution is upon us: first storage, then servers and applications, now data itself.  Data virtualization is critical in dynamic enterprises where business change necessitates rapid IT response, in large enterprises where data volumes and complexity are high, and in resource-constrained enterprises where total cost of ownership (TCO) is a key consideration.

 

 

 

What is Data Virtualization

Data virtualization is used to integrate data from multiple, disparate sources - anywhere across the extended enterprise - in a unified, logically virtualized manner for consumption by nearly any front-end business solution, including portals, reports, applications, search, and more.

As middleware technology, data virtualization is often referred to as virtual data federation, high-performance query or enterprise information integration (EII).  As IT architecture, data virtualization can be implemented as a virtualized data layer, an information grid, an information fabric, or a data services layer in service-oriented architecture (SOA) environments. It can be implemented for a single project or applied architecturally to a range of initiatives including Business Intelligence (BI) and Reporting, SOA Data Services, CDI MDM Single View and more.

 

 

How Composite Software’s Data Virtualization Works

Composite data virtualization address three fundamental challenges of data integration:

• Data Location. Data resides in multiple locations and sources
• Data Structure. Data isn’t always in the required form
• Data Completeness. Data frequently needs to be combined with other data to have meaning.

Composite data virtualization simplifies the location challenge by making all data appear as if it is available from one place, rather than where it is actually stored. Composite data abstraction simplifies data complexity by transforming data from its native structure and syntax into reusable views and Web services that are easy for business solutions’ developers to understand and business solutions to consume.  Composite data federation combines data to form more meaningful business information, producing a single view of a customer or a get inventory balances composite service, as examples.  Data can be federated from both consolidated stores such as the enterprise data warehouse as well as original sources such as transaction systems.

At design and build time, the Composite Information Server provides an easy-to-use data modeler and code generator that abstract data into relational views for reporting and other business intelligence (BI) uses or Web data services for SOA initiatives, portals, etc.  Composite Discovery helps by uncovering data and relationships from across the enterprise.

At run time, the Composite Information Server, along with Composite Active Cluster and Composite Application Data Services options, provide high-performance query capabilities that securely access, federate, transform, and deliver data to consuming business solutions whenever needed, 24 by 7.

 

How Virtual Data Federation Complements Physical Data Consolidation

Data virtualization is a critical element in any data integration strategy because it complements traditional data integration techniques such as physical data consolidation, messaging, and replication. 

 

 

There is no reason to be locked into one data integration method or another. In many cases, the best data integration solution is a combination of virtual and physical approaches. Here are some examples:

• Physical Data Warehouse and/or Data Mart Schema Extension. This is a way to extend existing schemas, such as adding current operations data to historical repositories.
• Physical Warehouses, Marts and/or Stores Federation. This is a way to federate multiple physical consolidated sources, such as two or more sales data marts after a merger.
• Data Warehouse and/or Data Mart Prototyping. This is a way to prototype new warehouses or marts, to accelerate an early stage leading into a larger BI initiative.
• Data Warehouse and/or Data Mart Source Data Access. This is a way to provide a warehouse or mart with virtual access to source data, such as XML or packaged applications that may not be easily supported by the current ETL tool, or to integrate readily available, already federated views.
• Data Mart Elimination. This is a way to eliminate or replace physical marts with virtual ones, such as stopping rogue data mart proliferation by providing an easier, more cost-effective virtual option.

Composite Data Virtualization in Action
By separating the logical from the physical, you can overcome source data complexities, reduce costs and develop new solutions faster. Here are a few of the hundreds of use cases that benefit both business and IT:

• Virtualized Financial Research Data.  Simplified integration of multi-terabyte financial research databases with a variety of Matlabs analytical and custom financial engineering applications results in higher trading profits.
• Scientific Research Workbench.  Heterogeneous research, clinical trial, FDA submission data and more virtualized and displayed in a research scientist portal helps get new drugs to market faster.
• Line of Business Data Virtualization.   Shared data services utility for all new SOA-based applications within the Corporate Investment Bank line of business accelerates time to market for new applications.
• Single View of Customer Trades and Positions. Unified reporting of customers’ trades and positions from across multiple assets (bonds, stocks, funds, derivatives, etc.) improves customer satisfaction and retention.
• Virtual Management and Compliance Reporting Layer.   Multiple reporting requirements (Prime Brokerage, Reconciliation, Risk Management, etc.) share a common virtual data layer that integrates source data from trading and other systems, on demand.

The Bottom Line
Data virtualization, following the proven path of storage, server, and applications virtualization, overcomes physical complexity to accelerate business initiatives and radically lower costs.  Composite data virtualization has been purpose-built to address today’s business challenges - enabling an agile approach that overcomes complexity, quickly providing business with the timely data it needs, even if the data required spans multiple silos and multiple geographies.