In our world of non-stop data transmission and high-speed information sharing, new tools are constantly appearing to aid in collecting, combining, and curating massive amounts of data.

The most recent innovation is Data Virtualization, a process that gathers and integrates data from multiple sources, locations, and formats to create a single stream of data without any overlap or redundancy.

With big data analytics, companies can locate revenue streams from existing data in storage, or they can find ways to reduce costs through efficiency. However, this is easier said than done. IT companies generally have multiple, dissimilar sources of information, so accessing that data can be time consuming and difficult. Data virtualization systems can help.

Companies that have implemented data virtualization software have better, quicker integration speeds and can improve and quicken their decision-making.

What is Data Virtualization

Data virtualization (DV) creates one "virtual" layer of data that distributes unified data services across multiple users and applications. This gives users quicker access to all data, cuts down on replication, reduces costs, and provides data flexible to change.

Though it performs like traditional data integration, DV uses modern technology to bring real-time data integration together for less money and more flexibility. DV has the ability to replace current forms of data integration and lessens the need for replicated data marts and data warehouses.

Data virtualization can seamlessly function between derived data resources and original data resources, whether from an onsite server farm or a cloud-based storage facility. This allows businesses to bring their data together quickly and cleanly.

How Virtualization Works

Most people who use IT are familiar with the concept of data virtualization. Let’s say you store photos on Facebook. When you upload a picture from your personal computer, you provide the upload tool with the photo's file path.

After you upload to Facebook, however, you can get the photo back without knowing its new file path. Facebook has an abstraction layer of DV that secures technical information. This layer is what is meant by data virtualization.

When a company wants to build Virtual Data Services, there are three steps to follow:

Connect & Virtualize Any Source: Quickly access disparate structured and unstructured data sources using connectors. Bring the metadata on board and create as normal source views in the DV layer.

Combine & Integrate into Business Data Views: Integrate and transform source views into typical business views of data. This can be achieved in a GUI or scripted environment.

Publish & Secure Data Services: Turn any virtual data views into SQL views or a dozen other data formats.

Once the DV environment is in place, users will be able to accomplish tasks using integrated information. The DV environment allows for the search and discovery of information from varied streams.

Global Metadata: Global information search capability lets users access data through any format from anywhere in the world.

Hybrid Query Optimization: Allows for the optimization of queries, even with “on-demand pull and scheduled batch push data requests.”

Integrated Business Information: Data virtualization brings users integrated information while hiding the complexity of accessing varied data streams.

Data Governance: DV layer serves as a unified layer to present business metadata to users. Simultaneously, it helps to understand the underlying data layers through data profiling, data lineage, change impact analysis and other tools and expose needs for data normalization / quality in underlying sources.

Security and Service Level Policy: All integrated DV data views can be secured and authenticated to users, roles and groups. Additional security and access policies can manage service levels to avoid system overuse.

Data Virtualization Tools

The various capabilities that Data Virtualization Delivers offers companies a newer, faster method of obtaining and integrating information from multiple sources. The top tools currently in use are as follows:

Logical abstraction and decoupling
Enhanced data federation
Semantic integration of structured & unstructured data
Agile data services provisioning
Unified data governance & security

These capabilities cannot be found organized in any other integration middleware. While IT specialists can custom code them, that minimizes the agility and speed advantages DV offers.

Data Virtualization creates many benefits for the companies using it:

Quickly combine multiple data sources as query-able services
Improve productivity in IT and by business data users (50%-90%)
Accelerate time-to-value
Improve quality and eliminate latency of data
Remove the costs associated with populating and maintaining a Data Warehouse
Significantly reduce the need for multiple copies of any data
Less hardware infrastructure

While this innovates new path to data collection and storage offers increased speed and agility, it is important to note what DV is not meant to be.

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