Timeplus com11/3/2022 TIMEPLUS COM HOW TOSo Event time will decide how to store the stream data. Both aged data and fresh data are required, so the data might be stored on different layers according to their age. While the aged data might also be helpful in some cases, for example, what if the user wants to compare the current trend with what happened at the same time last year. Event time is what the analytic system used to decide a specific event belongs to which window.Īs different aged data has different values, the analytic system wants users to fully leverage the value of newly generated fresh data, so high-performance streaming storage is required to store fresh data. With unbounded streaming analytics, users may still want to have an aggregated analysis result from time to time, using window-based aggregation is a common tool, such as tumbling or hopping windows. So the analytic system has to wait for the late event, and usually, the system does not know when these late events will arrive. A streaming analytic system has to handle these cases.įrom the above samples, you can see due to the network transmission, the event enters the system in a different order than the original event generating order. Real-world streaming data is usually non-perfect which means the event may enter the system with a long or short delay, events are coming out of order, the event time marked by the original system may have a different clock time than the processing system, all of these will make the time related processing more challenging. Here you can take event time as the birthday of the data. As we mentioned before, the event data has its life cycle, newly generated data are usually more valuable than aged data. To be specific, the Time here is the Event Time of each event.Įvent time is the time when the event is generated. Time is the most important characteristic of a stream. Time is essentialĮvent time is the most important attribute of streaming data The streaming process engine continuously runs the SUM operator and keeps posting the processing result to the user. the event in the stream contains a single value which could be sensor data, the event data is generated in real-time and it won’t stop. Here is an example of a streaming process for a simple analytic case where the user wants to know the total value of a number stream. To fully leverage the value of the data, the data processing needs to be in real-time. According to Pinterest, based on the data observed by their data analytic system, more than 98% of queries are on data age within 35 days. The value of data starts to decay quickly. The value of data is highly correlated to the age of the data, or the “freshness” of data. Data generation keeps accelerating, yet most data analytics systems haven’t kept up. You can see that every second, 20+ TB of data will be generated. The team at Penny Stocks Lab has designed an interactive infographic that visualizes what’s going on in the virtual world, every passing second - from YouTube videos to Google searches, from Instagram likes to every email sent. In this post, we’ll guide you through some of the core principles that we believe defines successful real-time analytics architecture, and how we’ve implemented those principles at Timeplus. Users need a system that can process, detect, and predict real-time information with the lowest latency and highest throughput. With the recent rapid evolution of real-time data sources, such as IoT, sensor technologies, wireless communications with 5G networks, powerful mobile devices, and electric vehicles, a more efficient way to process high-speed and real-time data stream is required.
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