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Today’s enterprise challenges demand near real-time access to data. While that seems to be a must, the application ecosystem continues to present a growing array of data persistence options, such as relational databases, NoSQL (not only SQL) databases, on-premise data warehouses, in-memory data stores — the list goes on.
Having built a career architecting and developing data integration strategies for enterprises, I am aware of the benefits of maintaining a local copy of data, but I also recognize that those also possesses considerable challenges, including:
• It’s hard to understand your data sources.
• There’s an undeniable lag in data movement.
• You need more lead time in designing and building data integration patterns.
• You also need infrastructure availability.
Depending on the robustness of the implementation and operationalization of your organization, the degree of the challenges mentioned above may lead to varied outcomes. My recent experience with data virtualization presents a unique approach and alternative solution to this problem depending on use cases.
Data virtualization (DV) is a data access platform that aggregates disparate data sources to create a single version of the data set for consumption. It provides a unified, abstracted, organized and encapsulated view of the data coming from similar or heterogeneous data sources while the data remains in source systems.
Data virtualization addresses the data movement challenge by ensuring data remains at the source — yet is also available for consumption in real-time for consuming applications. Its data collaboration approach allows an application to retrieve data as a single view component without the user requiring its technical details, such as its physical location, source formatting information, security parameters, configuration settings, etc. This platform substitutes extract-transform-loads (ETLs) and data warehousing in areas such as business intelligence and analytics, application development and big data consumption.
Data virtualization integrates data from diverse sources, locations and formats — without replication. A single “virtual” data layer is created in a process that delivers unified data services to support multiple applications and users while providing:
According to the Data Management Book of Knowledge: “data virtualization enables distributed databases, as well as multiple heterogeneous data stores, to be accessed and viewed as a single database. Rather than physically performing ETL on data with transformation engines, data virtualization servers perform data extract, transform and integrate virtually.”
Data virtualization capabilities allow IT departments to implement technology in their core business strategy to several benefits, including the following:
There are several ways to get started on your virtualization project. Consider these three:
Now you’re ready to implement your data virtualization project. To start, you can try one of the following approaches:
In the end, data virtualization’s location-transparent, built-in architecture, coupled with large-scale analytics architectures, naturally supports applications in a hybrid cloud environment. It goes beyond tiered views and delegable query execution to offer enterprise growth. Overall, implementing your own data virtualization approach will let you derive information faster.
POST WRITTEN BY
Director, Digital Modernization | Principal Architect | Technology Evangelist for Sage IT Inc.