![]() 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. Please check the Demo Scenario for a comprehensive list of our key capabilities.Life is short. Alerts are provided so that the users can make real time actions based on the anomaly detected by the streaming analytic result. The user can also use API to interact with the data or send the analytic result to downstream data systems such as Apache Kafka, Databases, Data warehouse or Data lakes. Real time visualization and dashboards are provided. ![]() Timeplus provides a web client where the user can interactively do data analysis in real-time. Timeplus supports various data source connections such as Apache Kafka, Amazon S3 and Amazon Kinesis. Timeplus is not only a streaming SQL database, it provides end to end analytic functionalities. You can join a stream with other streams, or enrich them with data from CSV, S3 or databases. Common streaming windows are supported such as tumble, hopping, session. Each query in Timeplus can detect late events, and you can choose to drop or wait for them. As our company name implies, we are specialized to process real-time data. Timeplus provides unique solutions to analyze both real-time data and historical data. Timeplus has a high performance streaming SQL engine, leveraging vectorized data computing capability, streaming data is processed in super high efficiency with the modern parallel processing technology Instruction/Multiple Data (SIMD). ![]() Timeplus Native log also supports timestamp based seek and optimized time series data analytics scenarios. Combined with TFF, Timeplus Native Log provides high performance data ingestion, it can quickly prune data on disk and filter out data which is not required for streaming processing. To fully leverage the capability of TFF, Timeplus also designed a stream storage called Timeplus Native Log. Since it is in column format, data can be vectorized for high performance analytic computation. Timeplus has designed a column based data format called Timeplus File Format (TFF), which supports blazing fast serialization and de-serialization. Timeplus is a fast and powerful real-time analytics platform.
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