Shift your Data into Virtualization

A single approach to data management that allows an application or user to retrieve and manipulate data without knowing any technical details about the data. That approach called Data Virtualization.

Data Virtualization is different than traditional virtualization like - VMWare, Hypervisor, KVM, etc. because we already learned how to do virtualization of OS, Hardware and Storage, now time to add some more into virtualization, which is DATA.

What is Data Virtualization?

Data virtualization is a single window used to describe any approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted, or where it is physically located.

Why use Data Virtualization?

Data virtualization promotes efficiency in data usage and processing and accelerates time to market on projects with complex data storage infrastructure. The purpose is to allow data to be accessed without creating extra work and complexity during processing of data requests. It also allows users to consume data without needing to be aware of its type or storage location. This is especially useful when applications use various data stores for data within one application or all of the data is stored in the cloud.

It is cheaper to maintain than traditional integration tools. A physically replication, moving and storing data multiple times is expensive. Where Data virtualization creates a virtual data layer which eliminates the need for replication or storage costs.

• It is a fastest way to manage data. Where a traditional data integration methods took a more wait time (in hours or even days) for the results, but Data virtualization provides results in real-time.
• It maximizes performance. It connects directly to the source and provides actionable insight in real-time.
• It enables self-service BI. It connects directly to the source and provide data without rely on the technical team.
• It ensures secure data governance. Data virtualization enables a centralized point of access to all kinds of information in the enterprise enabling security management, data governance and performance monitoring.
• It goes far beyond data federation. Data virtualization is a super-set of the ten year old data federation technology. It includes the advanced capabilities of performance optimization as well as self-service search and discovery.
• It is more agile than traditional methods. Data virtualization technology includes prototyping capabilities, meaning you can test out your strategy before implementing it on an enterprise scale.
• It gives the right context to big data fabric. Big data fabric enabled by Data virtualization integrates data, prepares it for predictive analytics and makes it available to the consumer in real-time.

 Key features of Data Virtualization

• Virtualized Data Access (Virtual Database) - The virtual data model provides a business-friendly representation of data, allowing the users to connect to different data sources and make them accessible from a common logical data access point.
• Improved information control - The metadata repository catalogues enterprise data stores and the relationships between the data in various data stores, enabling transparency and visibility.
• Data Security - Data Security should have a strong impact on how data virtualization security is managed (LDAP/AD, table-driven, basic, etc.). Data virtualization makes it much easier to expose a greater breadth of data and data sources to more users. Data Virtualization provides the benefit of being able to limit which rows a specific user or user group can see within a table. Information security may leverage this capability.
• Big data and cloud data integration - Data virtualization provides a rapid virtual integration approach that doesn’t require replication of already “big” data sources. Many organisations are adopting cloud computing where each new cloud source must be integrated with the existing IT environment. Data virtualization solves this problem, allowing enterprises to maintain a complete view of internal and external information while taking advantage of attractive cloud economics.
• Real-Time Data Access -  As source data is updated or changed, the data is available immediately.

 Some Populer Data Virtualization Tools

Denodo

The Denodo Platform provides all the benefits of data virtualization including the ability to provide real-time access to integrated data across an organization’s diverse data sources, without replicating any data. It provides query federation capabilities to support Logical Data Warehouse architecture while also isolating the user from the complexities – different technologies, formats, protocols, locations, etc. – of underlying data sources and provides a common and consistent view into the data.

TIBCO

TIBCO Software offers an enterprise data virtualization platform that access to multiple and varied data sources and delivers data sets that can help you reduce analytic data bottlenecks and deliver more insights and better business outcomes. This tool provides analytic access, and centralized metadata control that allows users to go beyond simple data profiling to examine data, locate important entities, and reveal hidden relationships across distinct data sources.

JBoss Data Virt

Red Hat's JBoss Data Virtualization is most popular open source product which offers comprehensive data abstraction, federation, integration, transformation, and delivery capabilities to combine data from one or multiple sources into reusable and unified logical data models that sits in front of multiple data sources and allows them to be treated as single source. It enables agile data utilization by providing following functionality:

• Connect: Access data from multiple, heterogeneous data sources.
• Compose: Easily combine and transform data into reusable, business-friendly virtual data models and views.
• Consume: Make unified data easily consumable through open standards interfaces.

JBoss Data Virtualization implements all three steps internally; hiding the technical complexities of knowing physical data locations, storage structures, APIs, access and/or merge logic, thus making it easy for developers and users to work with data.