Data Virtualization, a Necessary Component of Organizational Data Integration Strategy
Data virtualization receives its deserved recognition, as the market has significantly matured over the past few years. For the first time, Gartner has published a data virtualization specific Market Guide1, which particularly focuses on why data virtualization should be considered an important factor for organizations’ data integration strategy.
According to Gartner, “Data virtualization offers data and analytics leaders a data integration strategy to limit data silos and address new analytical and operational opportunities through flexibility in data access.”
Data Virtualization Steadily Becomes True Enterprise Class
Data virtualization is continuously growing in importance. Gartner estimates, “Near the end of 2011, only 11% organizations reported that they were utilizing data virtualization in a focused set of use cases. By the end of 2015, however, nearly 25% of organizations reported using data virtualization extensively and its use in creating an independent semantic tier has become a significant one. Gartner predicts that “data virtualization offerings are maturing at a steady pace in terms of connectivity options, performance, security and near-real-time data delivery. Current offerings are being increasingly deployed by all major verticals for production-level use cases… As an increasingly important part of a comprehensive data integration strategy, data virtualization is attracting renewed interest as organizations recognize its potential for a growing range of use cases.
The first generation of data virtualization tools were limited in capabilities. According to the report, in which Denodo is listed as a Representative Vendor, Gartner states that “Modern data virtualization tools provide both read and write access to a host of popular data types and sources. They provide additional features, including a metadata repository, abilities to persist transformed and federated queries, and advanced security and query processing features, which were previously missing in the first-generations data federation tools. Most of these opportunities involve augmenting the physically integrated data structures and providing consistent service-oriented approaches for applications and business services to access data.
In this report Gartner specifically focuses on the fact that “organizations are expanding their use of this solution beyond ‘limited’ development/test type deployments and are considering it as a real option for enterprise-class projects”. There are specifically two categories of use cases that Gartner has observed for data virtualization – analytical and operational – which greatly influence SLAs and data virtualization technology selection. “In an analytical context, data virtualization focuses on supporting faster business decision making by allowing users to quickly integrate and resolve data silos” and “in the operational context, data virtualization primarily supports business operations. It provides a reusable data access layer for operational applications and manages the complexity of diverse data sources”. Some of the notable analytical use cases are logical data warehouse, rapid prototyping of batch data movement, regulatory constraints and self-service analytics. On the other hand, from a business operational context, the most important use cases are virtual operational data store (ODS), reusable data services, legacy systems migration, and master data management.
Emergence of Four Distinct Data Virtualization Provider Models
Data virtualization products have extensively matured in three specific areas – “broad connectivity”, “distributed and optimized semantic tier processes”, and “maturity of development and administrative interfaces.” The more modern data virtualization tool offerings have native connectors to some of the most popular and critical wide variety of data sources and applications.”… The ability to now integrate with these diverse data sources for a 360-degree view of the business. Data virtualization market is also experiencing extensive maturity in terms of source systems access connections, data volumes that can be handled by these tools, network capacity, and query complexity. These advancements have increased adoption and implementation of data virtualization tools for mission critical workloads. Finally, the administrative and development interfaces have matured beyond simple developer tools to become more “citizen integrator” – friendly interfaces.
The resulting adoption and implementation of data virtualization tools have piqued interest of traditional DBMS, data integration and application tool vendors, who wants to play in the market. “Today, four distinct provider models for data virtualization technology have emerged.
- Standalone data virtualization providers.
- Data integration tool vendors.
- DBMSs with extensible data access.
- Application tool vendors
Denodo is a standalone data virtualization provider. Some of the key features of Denodo Platform to look for while evaluating various data virtualization solutions include: Dynamic Query Optimizer, Advanced Intelligent Caching, Unified Data Governance and Data Quality Management.
The Denodo Platform also offers advanced security features such as role based access control, integration with Kerberos and LDAP/Active Directory integration. Denodo Platform also differentiates itself by providing business users with self-service data discovery and exploration capability combines with google like search capabilities for data and metadata to find relevant information.
What Does This Mean for You?
If your organization does not have a data virtualization component in your data integration architecture, you should consider one. “Data virtualization offers an attractive alternative to bulk/batch data delivery by allowing flexibility and agility in data access through the creation of a single logical view of data from varied data silos (including transactional systems, RDBMSs, cloud data stores and big data stores)..”
You should evaluate your current information architecture minutely and understand your needs clearly. Set proper expectations upfront which may mean you need both physical data integration and data virtualization capabilities in your enterprise data architecture. Consider data virtualization as an important component of your overall data integration portfolio and plan accordingly. Build your future-proof information architecture to be more agile, flexible, adaptive and real-time, and in this architecture, data virtualization should take the center stage.
1 Gartner Market Guide for Data Virtualization, July 2016 Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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