Data virtualization is 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 at source, or where
it is physically located and can provide a single customer view (or single view
of any other entity) of the overall data.
An increasing need for data virtualization solutions,
automation, need to access large volumes of data stored across various systems
to get a consolidated view, reducing infrastructure costs, increasing need to
gain business insights, real-time availability of data, and an increasing
demand for data integration software tools are some of the major growth drivers
for the data virtualization Industry
Data virtualization can be deemed to be an alternative to
data warehousing and ETL.Unlike the traditional extract, transform, load
("ETL") process, the data remains in place, and real-time access is
given to the source system for the data. This reduces the risk of data errors,
of the workload moving data around that may never be used, and it does not
attempt to impose a single data model on the data (an example of heterogeneous
data is a federated database system). The technology also supports the writing
of transaction data updates back to the source systems. To resolve differences
in source and consumer formats and semantics, various abstraction and
transformation techniques are used. This concept and software is a subset of
data integration and is commonly used within business intelligence,
service-oriented architecture data services, cloud computing, enterprise
search, and master data management.
The major data virtualization vendors are International
Business Machines Corporation (IBM; US), SAP SE (Germany), Informatica (US),
Denodo Technologies (US), Oracle Corporation (US), TIBCO Software (US),
Microsoft Corporation (US), Red Hat (US), SAS Institute (US), and Information
Builders (US).
IBM is one of the leading vendors of the data
virtualization solutions. The company follows inorganic growth strategy such as
partnerships to enhance its products and solutions offerings in the data
virtualization market. For instance, in March 2017, IBM formed a partnership
with Salesforce to offer a solution by integrating capabilities of IBM Watson
and Salesforce Einstein to help accelerate decision-making process by combining
data from internal and external sources. IBM also launched a new product, IBM
Spectrum Protect Plus, to help organizations meet requirements of real-time
availability of data.
Another market-leading company, Denodo Technologies
adopted the inorganic growth strategy such as partnerships to gain a dominant
position in the data virtualization market. For instance, in February 2017,
Denodo partnered with Semarchy to enhance its capabilities on various fronts
such as marketing, sales, research, and partner development. Apart from that,
expansion can be seen as a significant strategy that Denodo has been following.
For instance, in August 2017, the company opened a new office in Singapore to
cater to the growing demands for data virtualization technology in APAC.
The objective of the study is to define, describe, and
forecast the global data virtualization market on the basis of components
(standalone software, data integration solution, and application tool solution)
deployment modes (cloud and on-premises), data consumer (Business Intelligence
[BI] tools, enterprise applications, and mobile and web applications),
enterprise size (large enterprises, and Small and Medium-sized Enterprises
[SMEs]), end users (manufacturing, healthcare, Banking, Financial Services, and
Insurance [BFSI], research and academia, media and entertainment, retail and
eCommerce, government and defense, and telecom and IT), and regions. The report
also aims at providing detailed information about the major factors influencing
the growth of the market (drivers, restraints, opportunities, and challenges).
Know More@ https://www.marketsandmarkets.com/Market-Reports/data-virtualization-market-261349988.html
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