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5 Key use cases of data virtualization

Use Cases of Data Virtualization

Data Virtualization Use Cases

With data volumes continuing to grow, enterprises must be able to manage all their data irrespective of where it resides. And they need a platform with agility to query data siloed across enterprise systems.

Enter data virtualization.

The technology enables businesses to handle Big Data, querying across data sources fast without the need to move data.

With data virtualization, data is integrated from disparate sources, multiple locations and in many formats in a single virtual data layer without replication. The benefits?

  • Access to data in near-real-time
  • Minimal data redundancy
  • Greater agility to change
  • Minimal lead time for designing and implementing for data availability

What are the main use cases of data virtualization (DV)?

Streamline Big Data projects

Sixty percent of Big Data projects fail, according to Gartner, though the reasons for failure vary.

Despite the potential for failure, enterprises begin Big Data projects to reap business benefits.

Data virtualization is capable of streamlining and increasing the chances of success of Big Data projects.

For example, with large volumes of Big Data stored in files such as .csv, .json, .avro and more, non-tech-savvy users might find it challenging to access data.

Data virtualization helps overcome this complexity by making Big Data available to a bigger business audience and to popular business intelligence (BI) tools.

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Modernizing data warehouse

For most companies, shifting their data warehouse to the cloud as part of a cost-cutting strategy is high on the agenda.

Firstly, companies are increasingly keen on maximizing the benefits of modernizing an ageing data warehouse when they migrate it to the cloud.

But they could face several issues. One of those is the potential need to create more data marts, which could result in more physical data stores, thus increasing costs.

Data virtualization helps overcome such issues in a couple of ways. It could be accommodated between BI tools and data warehouse, and data virtualization can also be leveraged to replace physical data marts with virtual data marts.

The result of this data warehouse modernization includes:

  • Lower total cost of ownership (TCO)
  • Increased agility
  • Eliminating inconsistency between data warehouse and data marts

Reimagined data lake

Physical data lakes showed promise to accommodate all data of organizations, but the process has proved challenging.

The same data lake could not store all the data, which led to siloed data lakes, making integration tougher. There was a lack of governance, while context and associations went missing too.

Data virtualization helps overcome the data silo issues with a superior data integration approach allowing for better business intelligence (BI), analytics and even machine learning (ML) and artificial intelligence (AI).

By removing the need to physically replicate data, data virtualization facilitates a logical data lake architecture, leveraging a virtual layer on top of the physical data lake.

This method provides the following advantages over physical data lakes:

  • Stakeholders can define complex, derived models which use data from connected systems while being aware of data transformation history, definitions and lineage
  • Logical data approach makes copying data an option and not a necessity
  • It is based on a Big Data system, the physical data lake, enabling it to smartly leverage processing power and storage capabilities

Reduce machine learning complexities

For many data scientists, data discovery and data integration remain major challenges when applying machine learning in the enterprise.

That’s backed up by a study which says data preparation is around 80 percent of work (60 percent of time on cleaning and organizing data, with a further 19 percent spent collecting data sets) for data scientists.

While the emergence of data preparation tools has made data integration easier for data scientists, some tasks still require advanced IT skills.

Data virtualization helps address those challenges with data discovery and data integration.

Using data virtualization, data scientists can have better access to key data, while adding new data is faster and cost-effective. In fact, some of the high-end data virtualization tools provide a searchable catalog of data sets, whose content can also be searched and queried.

Another key advantage of data virtualization for data scientists is that whether the data was stored in a relational database, a NoSQL system, in a SaaS application, or a Hadoop cluster, all data can be seen as if being stored in a relational database.

Self-service analytics

Data preparation for self-service analytics can also be accompanied by challenges. Data transformation tools may be limited, data types and formats may greatly vary, while users might also face data governance and data sharing issues.

Data virtualization enables users to overcome these challenges and prepare data sets from any raw data source.

Data virtualization tools also offer desired performance, higher security and data services to business users, who should be encouraged to perform self-service BI by providing seamless access to data, delivered securely with the help of a data virtualization layer.


Above mentioned are a few of the key use cases of data virtualization, which enables in-depth analysis through consistent, easily-comprehensible data, robust data security and governance, and flexibility to add or modify data sources as needed.

For organizations, adopting a solid data virtualization approach can help glean actionable information faster.

Author Bio: 

Suhith Kumar is a digital marketer working with Indium Software. Suhith writes and is an active participant in conversations on technology. When he’s not writing, he’s exploring the latest developments in the tech world.


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