![]() ![]() Skalierbarkeit: Bei Systemausfällen können die von den einzelnen Geräten kommenden Protokolldaten von einer Übertragungsrate von Kilobit pro Sekunde auf Megabit pro Sekunde ansteigen und zu Gigabit pro Sekunde aggregiert werden. This is where a full-fledged data streaming platform comes in. Where some real-time data processing is required for real-time insights, persistent storage is required to enable advanced analytical functions like predictive analytics or machine learning. Many companies are finding that they need a modern, real-time data architecture to unlock the full potential of their data, regardless where it resides. Latency must be guaranteed in millisecondsĬomplex computation and analysis of a larger time frame Latency needs to be in seconds or milliseconds More processing resources required to “stay awake” in order to meet real-time processing guarantees Less storage required to process current data packets. Less storage required to process the current or recent set of data packets. Most storage and processing resources requirement to process large batches of data. # Here’s a breakdown of major differences between batch processing, real-time data processing, and streaming data: An example would be for-real time application that purchases a stock within 20ms of receiving a desired price. Real-time data processing guarantees that the real-time data will be acted on within a period of time, like milliseconds.Streaming data processing means that the data will be analyzed and that actions will be taken on the data within a short period of time or near real-time, as best as it can. An example would be fraud detection or intrusion detection. This results in analysis and reporting of events as it happens. ![]() ![]()
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