Yearly Archives - 2025

The Role of Materialized Views in Modern Data Stream Processing Architectures + RisingWave

Incremental computation in data streaming means updating results as fresh data comes in, without redoing all calculations from the beginning. This method is essential for handling ever-changing information, like real-time sensor readings social media streams, or stock market figures. In a traditional, non-entrepreneurial calculation model, we need to process the entire dataset every time we get a new piece of data. It can be incompetent and slow. In incremental calculations, only the part of the result affected by new...

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Unlocking the Power of Patterns in Event Stream Processing (ESP): The Critical Role of Apache Flink’s FlinkCEP Library

We call this an event when a button is pressed, a sensor detects a temperature change or a transaction flows through. An event is an action or state change that is important to an application. Event stream processing (ESP) refers to a method or technique to stream the data in real-time as it passes through a system. The main objective of  ESP is to focus on the key goal of taking action on the data as it arrives. This enables real-time analytics...

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Real-Time Redefined: Apache Flink and Apache Paimon Influence Data Streaming’s Future

    Apache Paimon is made to function well with constantly flowing data, which is typical of contemporary systems like financial markets, e-commerce sites, and Internet of Things devices. It is a data storage system made to effectively manage massive volumes of data, particularly for systems that deal to analyze data continuously such as streaming data or with changes over time like database updates or deletions. To put it briefly, Apache Paimon functions similarly to a sophisticated librarian for our data....

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