What Exactly is a Unified Real-time Platform
URPs are motivated by escalating business demands for smarter operational applications that can leverage up-to-the-second data to make faster and better …
This big data technology lets users query a continuous data stream and detect conditions — within a few milliseconds to minutes — after receiving that data (i.e. flow of events); processing data in motion, or computing on data directly as it’s produced or received.
URPs are motivated by escalating business demands for smarter operational applications that can leverage up-to-the-second data to make faster and better …
A look at the four kinds of software that perform real-time analytics on event streams and when to use
Quick answer: No, you don’t always need data in motion to operate in real time. However, some high volume/low latency real-time systems do use data in motion …
AI and machine learning are going to shake up how companies operate, and streaming data amplifies the leverage that can be gained from these powerful
Promising developments, including KRaft, simplified protocols, two-phase commit support, Docker images, GraalVM support, and Kafka queues, will solidify …
Kafka Streams is pivotal to Kafka's evolution, enabling it to adapt to scalability and performance demands.
Today, Apache Kafka is a widely used technology, with applications ranging from real-time data processing to stream processing, event sourcing, and messaging …
Incorporating real-time data into mobile app development isn't just about delivering data faster. It's about creating more engaging, personalized, and …
Apache Druid is designed for speed, scale, and streaming data, which makes it well-suited for use when analyzing sensor data.
In this week's real-time analytics news: IBM announced it will launch a $500 million AI venture fund.