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Reading Time: 2 minutes With data ever increasing in volume, variety, and velocity, the pressure is on to be able to access data in real time, to serve increasingly demanding analytics. We need to be able to develop insights from data, and act on…
Reading Time: 5 minutes Bring any data to any data consumer, simply and easily: that’s the goal of data virtualization. Yet contrary to what may first come to mind, data consumers are more than simply BI, analytics, or data science applications. Just about every…
Reading Time: 2 minutes It’s common knowledge that a focus on customer service will yield significant, tangible results. How significant? Consider the success story of Ultra Mobile, a nationwide mobile virtual network operator (MVNO) that is focused on offering the best quality international voice…
Reading Time: 6 minutes In the previous article we reviewed the architectural elements of the virtual data lake, dug deeper into its technical implications. In this post, we will review the architectural elements of the virtual data lake, and will discuss why a virtual…
Reading Time: 3 minutes When E.F. (Ted) Codd developed the relational model in the early 1970s, he had a dream. He wanted applications to be independent of the data storage and data access layer. Changes made to one should not lead to changes to…
Reading Time: 3 minutes Many organizations deploy data virtualization servers in business intelligence (BI) systems to integrate data sources. In fact, this is such a popular use case that some mistakenly think that BI is its only use case. From the start, data virtualization…
Reading Time: 4 minutes After my previous posts about the performance of data virtualization in Logical Data Warehouse and Logical Data Lake scenarios, I have received similar feedback from many of you, “ that is all very interesting… but we want numbers!” Well, you…
