To manage large volumes of data from a variety of sources, an innovative data technique called “data virtualization” (DV) is used. Data virtualization is frequently used to collect and aggregate multi-source data in enterprise resource planning (ERP), customer relationship management (CRM), and sales force automation (SFA) systems.
From data collecting via multiple sources to advanced analytics, this system appears to be a one-stop shop. The primary value of DV, as viewed by organisations today, is the installation of this layer on top of existing data warehouses to enable rapid and reliable data access.
A Brief Overview of Data Virtualization
In What Is Data Virtualization, “data virtualization” is compared to a television guide that aggregates the material of numerous channels into a single location.
Simply said, DV enables the addition of a layer of data access between the data source and the user for speedier access. “Virtual data warehouse” and “virtual data lake” are two instances of DV.
Initially considered as a workaround for ETL, this technology has evolved to provide BI customers with rapid data access, data integration, data cleansing, and analytics capabilities. DV enables established technologies such as cloud, big data, and advanced analytics platforms to operate in unison to deliver superior data management solutions that traditional data warehouses were incapable of delivering.
Data Management in the Virtualization Era
Vendors are delivering a one-stop shop for data gathering, management, and delivery of data services via data virtualization platforms. According to a Wipro blog article, a significant strength of DV is the complete reliability and security of real-time data. This single benefit is assisting in generating enormous benefits in the shape of a rapidly growing DV market.
While ETL is still capable of handling large volumes of data, DV enables lightning-fast data access. At the moment, usage patterns indicate that enterprises are concurrently utilising DV and ETL.
Thus, what are the most evident benefits of using DV to manage enterprise data?
Rapid access to encrypted data
Data replication reduction
Utilization of a unified data service
Virtualization of Data for Big Data
Gartner forecasted that 60% of all big data projects would fail by 2020. While data virtualization cannot resolve all of the challenges associated with big data, it can significantly simplify operations and make big data projects easier to manage. To begin, this technology can make big data accessible and suitable for use on business intelligence systems.
The “volume, diversity, and velocity” of data stored in typical data warehouses is one of the key problems of big data. A logical data warehouse is an enterprise-wide data capture and organising solution for data that is organised, unstructured, batch, or real-time. Data virtualization can significantly minimise the amount of data integration required while maintaining performance.
The entire purpose of implementing such a data virtualization architecture is to enable the storage of “active data” in the warehouse and “dead data” in Cloudera-style repositories, and then to combine multi-source data via a logical data warehouse. The Data Virtualization vs. Copy Data Virtualization comparison demonstrates that, while consumers frequently misunderstand these two distinct ideas, there is a significant distinction between them.
Data must be retrieved considerably more quickly than is now achievable with advanced analytics or business intelligence tools. Thus, it is envisioned that DV will be increasingly used for enterprise-wide multi-source data integration, and that “unified data views” would enable users to obtain accurate information when it is required.
The article How Data Virtualization is Reshaping Analytics examines the benefits of the technology for corporate analytics. This cutting-edge technology overcomes the traditional restrictions of data preparation and analytics to produce the best results.
How Does DV Impact the Traditional Business Intelligence Landscape?
Through the use of a presentation layer and federated data, DV enables both rapid access to disparate data sources and a unified view of data. The encapsulated view of data enables business intelligence professionals to quickly develop dashboards with actionable insights. DV avoids data loss or inconsistency, which is particularly important when data is generated from streaming sources.
The following are some of the benefits that a typical business intelligence user finds when switching from a data warehouse to a DV architecture:
Increased access speed for real-time data
Risk of data loss or inconsistency is reduced
Reduced system effort Enhanced data governance via DV policies
Several downsides have been identified, including increased complexity in change management, the requirement for a superior Data Governance model, and the potential of affecting system response time. Despite its limitations, DV is suited for dynamic business intelligence and big data analytics.
Use Cases for Data Virtualization
Use Case 1: A virtual data warehouse is the preferred solution today since it is far faster to set up than a traditional data warehouse. This technology is well-suited for big data analytics and cloud-based business intelligence platforms.
Use Case 2: Instantaneous consolidation of data, regardless of source, is extremely valuable in a virtual data lake. This method of data access is advantageous for a wide variety of corporate users.
Cisco specialists feel that the true difficulty in a networked data world is not a shortage of data but rather the storage of data across multiple types of repositories, and that no amount of technology investment is worthwhile unless the generated information is valuable. They’ve proposed a DV solution that, according to the vendor, can convert “data stores into valuable information.” They believe DV has a future in addressing enterprise-wide data silos caused by IoT.
