Some use cases in data analysis require near-instantaneous processing of data. To make it happen, you need tools for real-time data analysis that support the use of continuous queries.
A streaming database is a real-time data repository specifically designed to store, accumulate, process and enhance a data stream.
Real-time graph analytics combines streaming data technology, graph databases, and graph algorithms to tackle problems not suited for relational databases and batch processing.
Stream processing is a type of big data architecture in which the data is analyzed in real-time
In this article, you can learn the difference between stream processing vs batch processing, and get to know the basic use cases for each.
Learn more about the benefits of real-time analytics and how they allow you to get key insights almost instantaneously.
Learn about the differences between static and dynamic data, real-time data sources and ways of analyzing streaming data
Learn how to pick the best streaming analytics tool for your use case
A basic overview of the architecture and features offered by Apache Pulsar and Apache Kafka
A short story about working with librdkafka
