The foundation of data virtualization technology is the execution of distributed data management processes, mostly for queries, against numerous heterogeneous data sources, and the Federation of query results into virtual views. Applications, query/reporting tools, message-oriented middleware, or other parts of the data management infrastructure then consume these virtual views. Instead of performing data movement and physically storing integrated views in a destination data structure, data virtualization can be utilized to construct virtualized and integrated views of data in memory. To make querying logic simpler, it provides an abstraction layer over the actual physical implementation of data.
It is a method for combining data from various sources and different types into a comprehensive, logical representation without physically relocating the data. Simply put, users can theoretically access and examine data while it still exists in its original sources thanks to specialized middleware.
Features of Data Virtualization
- Time to market acceleration from data to final product:- Virtual data objects can be created considerably more quickly than existing ETL tools and databases since they include integrated data. Customers may now more easily get the information they require.
- One-Stop Security:- The contemporary data architecture makes it feasible to access data from a single location. Data can be secured down to the row and column level thanks to the virtual layer that grants access to all organizational data. Authorizing numerous user groups on the same virtual dataset is feasible by using data masking, anonymization, and pseudonymization.
- Combine data explicitly from different sources:- The virtual data layer makes it simple to incorporate distributed data from Data Warehouses, Big Data Platforms, Data lakes, Cloud Solutions, and Machine Learning into user-required data objects.
- Flexibility:- It is feasible to react quickly to new advances in various sectors thanks to data virtualization. This is up to ten times faster than conventional ETL and data warehousing methods. By providing integrated virtual data objects, data virtualization enables you to reply instantly to fresh data requests. This does away with the necessity to copy data to various data levels but just makes it virtually accessible.
Layers of Data Virtualization
following are the working layers in data virtualization architecture.
- Connection layer: With the use of connectors and communication protocols, this layer is in charge of accessing the data dispersed across numerous source systems that contain both organized and unstructured data. Platforms for data virtualization can connect to various data sources, such as SQL and NoSQL databases like MySQL, Oracle and MongoDB etc.
- Abstraction layer: The abstraction layer, also known as the virtual or semantic layer, serves as a link between all data sources and all business users, forming the backbone of the entire virtualization system. This tier just holds the logical views and information required to access the sources; it does not itself store any data. The complexity of the underlying data structures is hidden from end users, who only see the schematic data models thanks to the abstraction layer.
- Consumption layer: A single point of access to the data stored in the underlying sources is offered by a different tier of the data virtualization architecture. Depending on the type of consumer, several protocols and connectors are used to give abstracted data representations. They can interface with the virtual layer using SQL and a variety of APIs, such as REST and SOAP APIs, as well as access standards like JDBC and ODBC. A variety of corporate users, tools, and apps can all have access to data virtualization software, including well-known ones like Tableau, Cognos, and Power BI.
Applications of Data Virtualization
- Migration: Think of a scenario where you migrate a CRM system to the cloud from a traditional one. Or a gradual migration of old systems to the cloud. You can accomplish this with data virtualization without halting operations or reporting.
- Uses In Operations:- For call centres or customer support systems, data silos are a big source of misery that have lasted for a very long time. A bank would, for instance, choose one call centre for credit cards and another for home loans. Data virtualization that spans data silos enables everyone, from a call centre to a database manager, to see the full range of data repositories from a single point of access.
- Agile BI:- With data virtualization, you can use your data for data science, API or system linkages, governed (regulated), and self-service BI. Additionally, it’s perfect for “agile” BI, which involves developing dashboards and reports in incredibly fast iterations that include testing, piloting, and production. Would you wish to add new sources to your current BI stream by connecting SaaS cloud services like Salesforce or Google Analytics? You may! You may combine all of your data with data virtualization, even in a hybrid environment. Additionally, you don’t need to worry about security because it is highly centralised.
-
Data Integration:- This is the most likely situation you will encounter because practically every company contains data from multiple different data sources. Connecting an antiquated client/server-based data source with modern digital platforms like social media is required for that.
You use the data catalogue to search your data after connecting using methods like Java DAO, ODBC, SOAP, or other APIs. Constructing connections is more likely to be difficult, even with data virtualization. - Accessing Real-Time Data:- Are your SLA agreements under pressure and a source system not performing adequately in terms of (near) real-time accessibility to massive amounts of data? Data virtualization allows you to blend real-time data from the source system with historical data that has been “offloaded” to a different source. You can prevent overtaxing your source systems by optimising your caching or conducting more intelligent system queries. Without initially copying every type of data with ETL operations, even near real-time analytics on huge data are feasible. Additionally, it is simple to create a virtual data mart by combining an outdated data warehouse with a fresh data source.
Advantages of Data Virtualization
- Data virtualization enables real-time access to and manipulation of source data through the virtual/logical layer without physically relocating the data to a new location. ETL is typically not required.
- Comparing the implementation of data virtualization to the construction of a separate consolidated store, the former takes less funding and resources.
- There is no need to relocate the material, and access levels may be controlled.
- Without worrying about a data type or where the data is located, users can build and execute whatever reports and analyses they require.
- Through a single virtual layer, all corporate data is accessible to all consumers and use cases.