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Data Virtualization and ETL: Friends or Enemies?

Click to learn more about author Ibrahim Surani. Traditionally, companies have relied on the use of Extract, Transform, Load (ETL) solutions to gather data from disparate sources and populate a data warehouse. However, increasingly complex IT infrastructures of businesses and the need to acquire near real-time or real-time data for accurate decision-making have compelled businesses […]

Make the Most of Graph Databases Through Interactive Analytics

New Big Data systems and advanced technologies are revolutionizing how businesses analyze their data assets and discover new value and insights across their business practices. Barry Zane, Senior Vice President and John Rueter, Vice President of Marketing at Cambridge Semantics, both recently sat down with DATAVERSITY® to discuss how companies are implementing new technologies such […]

Data Strategy vs. Data Architecture

“Data Leadership is about understanding the organization’s relationship with data and seeking ways to help the organization meet its goals using whatever tools are available,” said Anthony Algmin, of Algmin Data Leadership in a recent DATAVERSITY® interview. Within that overall Data Leadership Framework, sit Data Strategy and Data Architecture as individual disciplines. Data Strategy Companies […]

What is a Customer Data Platform?

Click to learn more about author Jonathan Lee. In the past year and a half DATAVERSITY readers have been exposed to new Customer Data Platform (CDP) offerings from vendors like BlueConic, RedPoint Global and others. But what is a CDP? First introduced around 2013, CDP is a concept defined by the Customer Data Platform Institute […]

Data Virtualization Defined: How it Helps Organizations Succeed

Data Virtualization (DV) is unlike traditional Data Integration, where change must be made on multiple layers; Data Virtualization makes change easy for the business as new requirements and sources can be integrated and changed rapidly. The Data Management Association International (DAMA) Data Management Book of Knowledge (DMBOK), second edition, describes Data Virtualization as: “Data Virtualization […]

A Brief History of Data Lakes

Data Lakes are consolidated, centralized storage areas for raw, unstructured, semi-structured, and structured data, taken from multiple sources and lacking a predefined schema. Data Lakes have been created to save data that “may have value.” The value of data and the insights that can be gained from it are unknowns and can vary with the […]

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