Difference between revisions of "Data appliance"
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Data Appliance is an engineered system of hardware and software optimized to capture and analyze the massive volumes of data generated by social media feeds, email, web logs, photographs, smart meters, sensors, and similar devices. | Data Appliance is an engineered system of hardware and software optimized to capture and analyze the massive volumes of data generated by social media feeds, email, web logs, photographs, smart meters, sensors, and similar devices. | ||
This definition can be applied to data management systems of all sizes. | This definition can be applied to data management systems of all sizes. The important message is that appliances can be installed in regular households, and allowing some level of automation, or so called: '''Lights out''' autonomy, therefore requiring much less professionally dedicated personnel to maintain their operations. As Microservices technologies becomes ever more mature, and computing devices are already pervasive and capable of running a wide range of data intensive computing and storage services, we can see that data appliance as an industry, will continue to reduce the required human labor to maintain the daily operations of these data appliances, which also meant that a significant amount of automated workflows across a large number of interconnected network devices could be operated by individuals or small organizations without having to pay regular fees to third party data service vendors. | ||
Latest revision as of 14:54, 19 February 2022
According to database vendor Oracle:
Data Appliance is an engineered system of hardware and software optimized to capture and analyze the massive volumes of data generated by social media feeds, email, web logs, photographs, smart meters, sensors, and similar devices.
This definition can be applied to data management systems of all sizes. The important message is that appliances can be installed in regular households, and allowing some level of automation, or so called: Lights out autonomy, therefore requiring much less professionally dedicated personnel to maintain their operations. As Microservices technologies becomes ever more mature, and computing devices are already pervasive and capable of running a wide range of data intensive computing and storage services, we can see that data appliance as an industry, will continue to reduce the required human labor to maintain the daily operations of these data appliances, which also meant that a significant amount of automated workflows across a large number of interconnected network devices could be operated by individuals or small organizations without having to pay regular fees to third party data service vendors.