DaaS

Showing 1 - 7 of 7 pages tagged with: DaaS

Myth Busters: I Access My Data Through APIs–Data Virtualization Can't Do This (Australia)

When you hear data virtualization, do you think BI and analytics? If so, you’re misinformed and missing out on a whole set of possibilities and capabilities of this technology. It is probably also why you think data virtualization is of no use to you if you need to access your data through APIs.This is why we’re back with another Lunch & Learn where we bust this myth!We’re going to enter the world, or ecosystem, of APIs and API-based architectures to investigate whether data virtualization plays a role in it. Maybe we’ll even learn that it possibly can enhance API capabilities and...

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Myth Busters IV: I Access My Data Through APIs–Data Virtualization Can't Do This

When you hear data virtualization, do you think BI and analytics? If so, you’re misinformed and missing out on a whole set of possibilities and capabilities of this technology. It is probably also why you think data virtualization is of no use to you if you need to access your data through APIs.This is why we’re back with another episode of Myth Busters!We enter the world, or ecosystem, of APIs and API-based architectures to investigate whether data virtualization plays a role in it. Maybe we’ll even learn that it possibly can enhance API capabilities and increase the benefits?Here’s what we’...

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Three Dimensions of Data as a Service (Middle East)

Today’s evolving data landscape has spawned new business challenges that require innovative solutions. These challenges include:Strategic decision-making, which relies on multiple perspectives such as social and economic factors that require combining internal and external data.Accounting for the increased volume and structural complexity of today’s data, and increased frequency required in delivering data assets.Coping with data silos that house data that must be combined and provisioned to support decision-making.Exposing purpose-built analytics, such as supply chain, for consumption in...

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Leading Construction Equipment Manufacturer Improves Service Delivery and Revenue Using Data Virtualization

To engage in proactive field maintenance and service delivery, The Company heavily invested in modern tools and technologies for telematics and predictive analytics. In addition, since The Company depends heavily on part data from the field for predictive analytics, it invested in field sensors and big data technology.All this data, captured several times an hour, is then streamed or transferred into a data lake. The company needed an agile data integration and access layer, one that can easily integrate big data with other sources of enterprise or cloud data in real time. Download this case...

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Multi-zone Data Virtualization for Data Lakes

Paul Grooten, Data Architect, Statistics Netherlands presents how Statistics Netherlands are using a Spark-based data lake with Denodo's data virtualization in a multi-zone architecture to enable the statisticians to quickly find and use the data in the format they need.

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Data-as-a-Service (DaaS) for Business Decision Makers

Leverage Data Virtualization to Turn Raw Data into Actionable IntelligenceBusiness decision makers are not analysts, but they are often called upon to play the role of analysts by poring over tables of raw data to derive actionable insights. Ultimately, business decision makers need more than raw data; they need clean, curated information that is custom-fi t to their goals, so that when it arrives, it is immediately intelligible. In short, what they need is data-as-a-service (DaaS).Download this solution brief to learn more about:

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Supporting Data Services Marketplace using Data Virtualization

Data is treated truly as an asset at Guardian Life. We have created a Data Services Marketplace which contains valuable data from the underlying sources and is used by business users for day-to-day operations. In this presentation we will talk about how Data Virtualization can be used to support the marketplace with real-time data services, provision non real-time data into Hadoop, and swap underlying sources without affecting business users.

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