Companies now compete in the area of information management. Companies can only be successful if they know how to use their data in the best possible way. For this, companies have to face some technical challenges and that is not an easy endeavor.

 

Data is distributed over many applications that follow a monolithic approach to data management. There are also numerous other data sources that are used for a wide variety of purposes. Data is distributed across different locations and the cloud, and IoT projects ensure that the amount of data increases exponentially.

 

Traditional data integration via data warehousing and extract, transform and load (ETL) processes is too rigid, too costly and not easy to change to support the new requirements for agility and speed. You can only remain competitive if you take a better path: data virtualization.

 

Data virtualization is not reinventing the wheel. Rather, as a further development of virtualization, it offers an alternative approach to significantly reduce the time required to implement projects in the area of information management and to increase agility.

 

Data virtualization can make data from different data sources available in one or more virtual data models suitable for the respective application. It is provided via database protocols (SQL, ODBC , JDBC , ADO.NET), web services (HTTP, REST, SOAP, XML, JSON, OData) or messaging (JMS). This means that data from a wide variety of data sources can be made available quickly and easily for application development, integration or data analysis. The data is cached for high-performance access and can be synchronized with the original data source as required. Avoiding copies of the data improves data quality and integrity. Central access management means that only authorized employees have access to the relevant data.

 

Last but not least, application data decoupled from the infrastructure have a significant positive effect on its speed. A lower load is also reflected in reduced bandwidth, storage and license costs and thus ensures noticeable savings in infrastructure costs.

 

A traditional enterprise data warehouse (EDW) is a time-consuming process for companies. Companies usually need a complete overview of their data more quickly in order to make better decisions. With a Data Virtualization Suite, the implementation time is significantly reduced compared to EDW. Such a suite also has automated tools and a simple user interface.

 

Data virtualization allows direct access to company-internal data. This gives companies more insight into their company. The required data can come from various real-time data sources such as databases, web content and other application environments.

 

A traditional enterprise data warehouse will not function without system resources, storage space, network traffic and data management overhead. In addition to the IT costs, all of these elements also significantly increase the system load. Data virtualization, used within a business intelligence system, enables a simpler architecture to be developed. This means less data duplication and thus simplified management.

 

The agile integration approach offered by data virtualization gives companies deeper insights into their data. The technology behind it can also react faster to ever faster analyzes and smarter business requirements. On average, companies benefit from a solution time that is five to ten times faster than with conventional data integration.

 

With the enormous amount of data that companies can process today, how companies handle this data is crucial. The better the data is used, the higher the value for the company and that often makes the decisive difference in the competition.

 

At Alibaba, we will assist you with data virtualization that will allow access to a wide variety of company-wide data storage systems in real time. It paves the way for companies to react quickly to requirements and changes that can give companies a decisive competitive advantage.