Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
TinyML (Tiny Machine Learning) is a technology that enables machine learning models to be run on resource-constrained microcontrollers and embedded devices.
Autonomous systems are technological frameworks that can execute specific tasks, adapt to environmental changes, and make independent decisions without direct human control or intervention. Utilizing sensors, artificial intelligence algorithms, machine learning, and data analytics capabilities, these systems perceive their environment, assess situations, and take appropriate actions.
Cascading is a platform for developing big data applications on Hadoop.
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