#data_streaming
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Follow-up to the Sho Joker render.
Credits:
Model - me, ATLUS
Cyber Ring - Duekko
Effects: B-ToonShader, ExcellentShadow, AutoLuminous, Mega IkClut Pack [RaC], o_SelfOverlay_改変 微彩, ikDiffusion, ikClut_デフォルメ, ikClut改変 cinema, Data_stream, NEXT_Solid, sdExpensiveLens, msVignetting
#mikumikudance#mmd#render#my renders#3d#labrys#oracle#persona 5#persona#persona 4 arena ultimax#i saw the turtleneck extending to futaba's chin then looked at labrys and thought#“i can make this work”#and I could#better life choices have been made cause the mask was a pain
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Time series data present an additional layer of complexity. By nature, the value of each time series data point diminishes over time as the granularity of the data loses relevance as it gets stale. So it is crucial for teams to carefully plan data integration strategies into time series databases (TSDBs) to ensure that the analysis reflects the trends and situation in near real-time. In this article, we'll examine some of the most popular data integration solutions for TSDBs
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Data streaming with Kafka is suitable for sports data. Kafka can collect and process real-time data from multiple sources (such as sensors, mobile devices, web applications, and more) and store it in a persistent, secure, and scalable way. Furthermore, Kafka's high throughput makes it an excellent fit for sports data, as it can handle large volumes of data and processing events in real-time. This article will show us how real-time data streaming with Apache Kafka leads to innovation in the Sports and Gaming industry.
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