How to Leverage Data Virtualization and Accelerate Machine Learning
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How to Leverage Data Virtualization and Accelerate Machine Learning

Data is undoubtedly a priceless resource. However, managing it is super complex. This complexity even grows more when there is more information flowing into the data ecosystem.  

To handle the complexity, organizations often employ a highly distributed data ecosystem. Here, they consolidate data into a single system like a data lakehouse or data lake  supporting varied data initiatives like machine learning and advanced analytics.  

Storing data of all types, be it structured or unstructured, in a single system reduces the time spent on integrating data while offering immense processing power. But data science practitioners often spend time to wangle and cure data.  

A Single Repository Doesn’t Guarantee Simple Discovery 

Using multiple systems to handle data analytics could be costly and complex. In that case, using a single platform that manages everything makes sense. However, having all the data in a single place doesn’t guarantee a simple discovery. It may resemble something like finding a needle in a haystack. 

According to Gartner, “A single data persistence tier and type of processing is inadequate when trying to meet the full scope of modern data and analytics demands.” For instance, if you closely examine a cloud provider’s reference architecture, they usually offer varied processing engines based on the type of data types or tasks. 

Besides, there are also chances that data may not be unusable when it is stored in raw form. In that case, you’ll have to modify, transform or prepare it before using it for machine learning methods. However, methods like data virtualization can solve the problem by reducing data science workloads and enabling companies to capitalize on data lakehouse or existing tech investments. 

Data Doesn’t Require a Destination 

With data virtualization, data scientists can access information in a format suitable for their needs. They don’t always have to move or replicate data into a single repository. The information can easily remain at the source and serve various business needs simultaneously. 

Data virtualization also offers an easy and cost-effective way of using data and meeting the needs of applications and users. This can help in resolving issues that many data science practitioners face. 

Using a Logical Approach 

Using data virtualization and a logical-first approach can effectively minimize data preparation efforts, time to value and delivery times. According to Forrester, data preparation efforts are minimized by 67% by employing and maintaining a logical approach. 

Furthermore, using a logical approach also enables efficient labor division between data engineers and scientists. They can build reusable sets of logical data that expose information by using data virtualization in ways suitable for niche applications. 


Data virtualization will gradually become crucial for improving the results of machine learning initiatives as cloud adoption will grow and data lakes will become more popular. By leveraging data virtualization, data scientists can eliminate the burden of data administration from their shoulders. Moreover, they can take advantage of data discovery based on catalogs while streamlining data integration and preparation efforts.  

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