Difference between revisions of "Data Revolution"
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The Structure of Data Revolutions can be a great book title, paraphrasing Kuhn's [[The Structure of Scientific Revolutions]]. | The Structure of Data Revolutions can be a great book title, paraphrasing Kuhn's [[The Structure of Scientific Revolutions]]<ref>{{:Book/The Structure of Scientific Revolutions}}</ref>. An essential idea in Kuhn's book, was that the frequency of terms being cited in a society can be reliably assessed by a connected computing system, such as [https://trends.google.com/trends/?geo=US Google Trends] and [[Matomo]]. This kind of capability will enable quantitative analysis of [[paradigm shift]] at a very large scale. Therefore, a new era of data revolution can take place, but still follow the original thesis of Kuhn's early vision. | ||
=The maturity of DevOps is a key indicator of Data Revolution= | |||
It would be useful to think of version control in [[DevOps]] as an operational pattern to cope with data revolution. Without a scalable solution to data versioning and data deployment process, it would be particularly difficult to create systems that could survive the fast pace of technological revolution. It would be reasonable to say that the degree and scope of an integrated [[DevOps]] model, directly govern the speed of data revolution. The workflow and data integrity of [[DevOps]] determines how fast and how controllable new features and new ideas can be introduced to various application areas. | |||
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=References= | =References= | ||
Latest revision as of 12:10, 21 January 2022
The Structure of Data Revolutions can be a great book title, paraphrasing Kuhn's The Structure of Scientific Revolutions[1]. An essential idea in Kuhn's book, was that the frequency of terms being cited in a society can be reliably assessed by a connected computing system, such as Google Trends and Matomo. This kind of capability will enable quantitative analysis of paradigm shift at a very large scale. Therefore, a new era of data revolution can take place, but still follow the original thesis of Kuhn's early vision.
The maturity of DevOps is a key indicator of Data Revolution
It would be useful to think of version control in DevOps as an operational pattern to cope with data revolution. Without a scalable solution to data versioning and data deployment process, it would be particularly difficult to create systems that could survive the fast pace of technological revolution. It would be reasonable to say that the degree and scope of an integrated DevOps model, directly govern the speed of data revolution. The workflow and data integrity of DevOps determines how fast and how controllable new features and new ideas can be introduced to various application areas.
References
- ↑ Kuhn, Thomas (2012). The Structure of Scientific Revolutions (50th Anniversary ed.). local page: University of Chicago Press. ISBN 978-0-226-45811-3.