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Virtual data warehousing: Even more efficient data processing

Virtual data warehousing: Even more efficient data processing

For companies who want to retain their position as a digital leader and keep pace with technological progress in our modern world, the accurate and efficient processing of data is now more critical than ever before. Data lakes are one tool that enables users to collate all the data they hold in a single location. In this post, we introduce an even more extensive method that allows users to maintain performance and efficiency in incoming data flows and retain complete flexibility during data processing: Virtual data warehouses. 

What is a virtual data warehouse?

Since the 1980s, data warehouses have been deployed to store data in a structured manner – although back then, the data was still in analog format. Today, with big data delivering vast quantities of unstructured information virtually, the system has changed. Rather than storing incoming data in individual silos, data warehouses collate the data in a database. Unlike data lakes, data warehouses place the data in hierarchical files and folders. 

Since 2014, companies have been starting to make use of virtual data warehouses, and the solution is increasingly playing a vital role as an element of a traditional business intelligence (BI) architecture. The solution is helpful when users need a 360-degree overview of their customers and a consolidated data storage solution with defined ETL data processes. 

How does virtual data warehousing work? 

The key concept in virtual data warehousing technology is visualization. At the core of the virtual data warehouse is data visualization: a technology that divides the physical environment into logical units, creating an integrated layer of semantic metadata and logic which allows the various data sources (which are usually distributed) to be abstracted from the data. All applications and services can then draw on this shared starting point (known as a repository). The beneficiary of the repository may be, for example, a cloud that provides different users with access to metadata and all available data sources via simple data processing tools, while simultaneously orchestrating this access and optimizing the performance of queries and other operations within the virtualized data files.

With this technology, even smaller units within a company – such as individual specialist departments – are able to combine pre-cataloged data from a data warehouse with real-time data from sensors, the cloud, mobile applications or production, and use this data to produce comprehensive analyses. The analysis process can be accelerated yet further, and adjusted to the specific needs of the company, with machine learning

What advantages does virtual data warehousing bring for a company? 

Virtual data warehousing not only supports the self-service BI and the implementation of data-driven solutions, but also the work of developers, for example by providing secured sandboxes. According to Gartner, the visualization of data sources brings countless economic benefits, and enables companies to benefit from agile application development for big data and business analytics. With this technology in place, companies can ensure that they are optimally prepared for the challenges posed by the Internet of Things, machine learning and future technological innovations. The use of machine learning in particular is on the rise – and, as our latest study shows, the technology is fast becoming an essential tool for companies who want to retain or improve on their market position in the future.

This may interest you too: 
Data lakes: The bedrock of Big Data processing
Future investment in artificial intelligence
The varieties and opportunities of artificial intelligence
Out now: study on artificial intelligence and machine learning in companies

 

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