Tag: Data Science

A Q&A on Big Data in the Cloud

Chip Maurer Leave a comment

The title of our recent live SNIA Cloud Storage Technologies webcast, “Cloud Storage and Big Data, A Marriage Made in the Clouds” might lead you to believe we were producing a new reality show, but of course, that was not the case. This webcast with SNIA experts, Chip Maurer, Vincent Hsu and Andy Longworth examined modernization challenges related to Big Data and key considerations for storing Big Data as workloads evolve. Our audience asked great questions during the live event. As promised, here are our experts’ answers.

Q: Is there much movement with Open Source Object Storage solutions, such as OpenStack suite – Swift, etc?

A. Yes, there is no shortage of Open Source storage solutions. The decision depends upon your organization’s expertise, reliability, cost, application availability and location, and your overall storage strategy.

Q. What drives organizations to modernize?

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Achieving Data Literacy

Jim Fister Leave a comment

We’re all spending our days living in the pandemic and understanding the cultural changes on a personal level.  That keening wail you hear is not some outside siren, it’s you staring out the window at the world that used to be.  But with all that, have you thought about the insight that you could be applying to your business?

If the pandemic has taught data professionals one essential thing, it’s this:  Data is like water when it escapes; it reaches every aspect of the community it inhabits. This fact becomes apparent when the general public has access to statistics, assessments, analysis and even medical journals related to the pandemic, at a scale never seen before.

But having access to data does not automatically grant the reader knowledge of how to interpret that data or the ability to derive insight. In fact, it can be quite challenging to judge the accuracy or value in that data.

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The Power of Data Aggregation during a Pandemic

Glyn Bowden Leave a comment

The new coronavirus that has been ravaging countries and sending us all into lockdown is the most observed pandemic we’ve ever experienced. Data about the virus itself and perhaps more appropriately, the nations upon which it is having an impact have been shared from multiple sources. These include academic institutions such as John Hopkins University, national governments and international organisations such as the World Health Organisation. The data has been made available in many formats, from programmatically accessible APIs to downloadable comma delimited files to prepared data visualisations. We’ve never been more informed about the current status of anything.

Data Aggregation

What this newfound wealth of data has also brought to light is the true power of data aggregation. There is really only a limited number of conclusions that can be drawn from the number of active and resolved cases per nation and region. Over time, this can show us a trend and it also gives a very real snapshot of where we stand today. However, if we layer on additional data such as when actions were taken, we can see clear pictures of the impact of that strategy over time. With each nation taking differing approaches based on their own perceived position, mixed with culture and other socio-economic factors, we end up with a good side-by-side comparison of the strategies and their effectiveness. This is helping organisations and governments make decisions going forward, but data scientists globally are urging caution. In fact, the data we are producing today by processing all of these feeds may turn out to be far more valuable for the next pandemic, than it will for this one. It will be the analysis that helps create the “new normal.”

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