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How data virtualization works as a service container, as a source for microservices and as an API gateway.
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Today, businesses have more data and data types combined with more complex ecosystems than they have ever had before. Examples include on-premise data marts, data warehouses, data lakes, applications, spreadsheets, IoT data, sensor data, unstructured, etc.
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The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
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Data mesh is a new decentralized paradigm for data analytics that aims to remove bottlenecks and take data decisions closer to those who understand the data.
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Data holds endless potential for agencies and companies. But if not properly shared, analyzed and visualized, that potential cannot be reached. As the federal data strategy encourages building a culture that values data and promoting public use, agencies are learning how to effectively prepare data for sharing and to convey insights — and traditional approaches won’t maximize their efforts.
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The Virtual Sandbox is an overarching framework to support the enterprise-scale roll out of data science programs using the industry standard, CRISP-DM methodology.
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Reporting tools, while important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data. Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.
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