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 […]

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 […]

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 […]

Revolutionize Stream Processing with the Power of Data Fabric

A data fabric is an innovative system designed to seamlessly integrate and organize data from multiple sources, making it easily accessible, usable, and shareable. Think of it as a connected […]

Which Flow Is Best for Your Data Needs: Time Series vs. Streaming Databases

Data is being generated from various sources, including electronic devices, machines, and social media, across all industries. However, unless it is processed and stored effectively, it holds little value. A […]

Integrating Apache Kafka in KRaft Mode with RisingWave for Event Streaming Analytics

Over the past few years, Apache Kafka has emerged as the top event streaming platform for streaming data/event ingestion. However,  in earlier version 3.5 of Apache Kafka, Zookeeper was the […]

RisingWave- An unwrinkled road to event stream processing

A distributed SQL streaming database called RisingWave makes processing streaming data easy, dependable, and efficient. Businesses can quickly and effectively respond to user behavior patterns by utilizing real-time analytics. This […]

Transferring real-time data processed within Apache Flink to Kafka

Transferring real-time data processed within Apache Flink to Kafka and ultimately to Druid for analysis/decision-making. Businesses can react quickly and effectively to user behavior patterns by using real-time analytics. This […]

Leveraging Apache Kafka for the Distribution of Large Messages (in gigabyte size range)

In today’s data-driven world, the capability to transport and circulate large amounts of data, especially video files, in real-time is crucial for news media companies. For example, an incident occurred […]

Understanding of Supervisor and it’s specification in Apache Druid for real-time data ingestion from Apache Kafka

Although both Apache Druid and Apache Kafka are potent open-source data processing tools, they have diverse uses. While Druid is a high-performance, column-store, real-time analytical database, Kafka is a distributed […]