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This IT model starts with datacenters built with extreme power efficiencies (PUEs reaching 1.2 or less), extends to open hardware projects (like Open Compute), and reaches into the transformation to a scale-out software architecture model (epitomized by "big data" projects like Hadoop and Cassandra)." It's in this "manifest disruption," that startups thrive.
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Table 1 provides some high level sample statistics; Table 2 focuses on digitization and big data projects by telecom companies.
In addition to the projects supported by Apache, there are other open source big data projects, such as Cloudera Impala [28] and RHIPE [29].
So, if data is not the key to a good project, what is? "Too many big data projects are started by the IT departments in companies that want to play with new technologies like Hadoop," says Dr Laurie Miles, head of analytics at big data specialist, SAS.
Note finally that some other pain-points may be visible in big data projects but, which were not measured by our telecom sample.
Big data projects based on the data collected by providers in EMR, claims, registries, and active patient monitoring are providing valuable information on many aspects of bipolar disorder for research and clinical care.
Gartner estimates that 85% of big data projects fail.
Atanas and Martin have been working together on big data projects for seven years already.
Digitization helps adopting big data, provided that big data projects are well managed. .
Table 3 Frequency of bottlenecks encountered in big data projects.
An important first step in scoping out these big data projects is to calculate how much data you've got then multiply by a thousand.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com