Data Virtualization Features, Advantages And Use Cases
Contents
What is Data Virtualization?
The execution of distributed data management procedures, primarily for queries, against several heterogeneous data sources and the federation of query results into virtual views form the basis of data virtualization technology. These virtual views are subsequently consumed by applications, query/reporting tools, message-oriented middleware, or other components of the data management architecture. Data virtualization can be used to create virtualization and integrated views of data in memory rather than transferring data and physically storing integrated views in a destination data structure. It offers an abstraction layer over the actual physical implementation of data to simplify querying logic.
It is a technique for integrating data of many kinds and sources into an all-encompassing, logical representation without having to move the data physically. In other words, customized middleware allows users to potentially access and analyze data while it is still in its original sources.
Features of Data Virtualization
Accelerating data-to-product time
Because virtual data objects have integrated data, they can be produced significantly faster than current ETL methods and databases. Consumers can now obtain the information they need more readily.
One-Stop Security
It is possible to access data from a single location with modern data architecture. The virtual layer that provides access to all organizational data enables data security down to the row and column level. Data masking, anonymization, and pseudonymization make it possible to authorize multiple user groups on the same virtual dataset.
Clearly combine data from diverse sources
Distributed data from cloud solutions, data lakes, big data platforms, data warehouses, and machine learning may be easily integrated into user-required data objects with the virtual data layer.
Flexibility
Rapid response to industry changes is feasible with data virtualization. Compared to traditional ETL and data warehousing techniques, this is up to ten times faster. Data virtualization allows you to respond immediately to new data demands by offering integrated virtual data objects. This merely makes the data virtually accessible and eliminates the need to replicate it to different data levels.
Layers of Data Virtualization
The working layers of the data virtualization architecture are as follows.
Connection layer
This layer is responsible for accessing the data scattered across multiple source systems that contain both organized and unstructured data through the use of connectors and communication protocols. Numerous data sources, including SQL and NoSQL databases like MySQL, Oracle, and MongoDB, can be connected to via data virtualization platforms.
Abstraction layer
The foundation of the entire virtualization system is the abstraction layer, sometimes referred to as the virtual or semantic layer, which acts as a conduit between all data sources and all business users. This layer does not store any data; it just contains the information and logical views needed to access the sources. Because of the abstraction layer, end users only see the schematic data models and are unaware of the intricacy of the underlying data structures.
Consumption layer
A separate tier of the data virtualization architecture provides a single point of access to the data kept in the underlying sources. A variety of protocols and connectors are used to provide abstracted data representations, depending on the type of consumer. SQL and a number of APIs, including REST and SOAP APIs, as well as access standards like JDBC and ODBC, can be used to communicate with the virtual layer. Data virtualization software can be used by a wide range of corporate users, tools, and applications, including popular ones like Tableau, Cognos, and Power BI.
Use Cases of Data Virtualization
Migration
Consider a situation where a CRM system is being moved from a traditional one to the cloud. or a slow cloud migration of outdated systems. Data virtualization allows you to do this without stopping reporting or operations.
Uses In Operations
Data silos have long been a major source of annoyance for call centers and customer support systems. For example, a bank might designate one call center for home loans and another for credit cards. Everyone, from a database manager to a call center, can view the entire array of data repositories from a single point of access with data virtualization that crosses data silos.
Agile Business Intelligence
Data virtualization allows you to use your data for self-service BI, governed (regulated), data science, and API or system connections. It’s also ideal for “agile” business intelligence, which entails creating dashboards and reports in lightning-fast iterations that encompass testing, piloting, and production. Would you like to link SaaS cloud services like Google Analytics or Salesforce to bring new sources to your existing BI stream? Yes, you can! Even in a hybrid environment, you can use data virtualization to merge all of your data. Additionally, because it is very centralized, security is not a concern.
Data Integration
Since almost every business has data from a variety of sources, this is the most likely scenario you will run into. For that, a client/server-based data source that is outdated must be connected to contemporary digital platforms such as social media.
Once you’ve connected using techniques like Java DAO, ODBC, SOAP, or other APIs, you can search your data using the data catalogue. Even with data virtualization, link construction is more likely to be challenging.
Accessing Real-Time Data
Does a source system’s performance in providing (near) real-time accessibility to vast volumes of data put pressure on your SLA agreements? You can combine historical data that has been “offloaded” to a separate source with real-time data from the original system with data virtualization. By improving your cache or using more sophisticated system queries, you can avoid overtaxing your source systems. Even near real-time analytics on massive amounts of data are possible with ETL processes without first copying all types of data.
Additionally, merging a new data source with an old data warehouse makes it easy to establish a virtual data mart.
Advantages of Data Virtualization
- Through the virtual/logical layer, data virtualization allows for real-time access to and alteration of source data without requiring the data to be physically moved. Usually, ETL is not necessary.
- The installation of data virtualization requires less cost and resources than building a separate consolidated store.
- The material doesn’t need to be moved, and access levels may be managed.
- Users can create and run any reports and analyses they need without thinking about the type of data or where it is stored.
- All customers and use cases have access to corporate data through a single virtual layer.