Techniques in Real-Time Data Processing
Multiple approaches allows for easy handling of real-time data, such as:
- Stream handling: This includes constantly handling data streams gradually. Tools like Apache Kafka, Apache Flink, and Apache Storm are widely used for stream processing.
- Complex Event Processing (CEP): CEP finds patterns and links in real-time data to spot important events and trends. Tools such as Apache Esper and IBM’s Operational Decision Manager allow CEP.
- In-Memory Computing: By utilizing memory (RAM) to store and process data, in-memory computing reduces delay and improves working speed. Technologies like Redis and Apache Ignite show this way.
What is Real Time Processing in Data Ingestion?
The ability to handle data as it is generated has become increasingly important. Real-time data handling stands out as a strong method that allows instant decision-making, business efficiency, and improved user experiences.
In this article, we looks into the idea, uses, methods, design, benefits, obstacles, and future trends of real-time data processing.
Table of Content
- Understanding Real-Time Data Processing
- Techniques in Real-Time Data Processing
- How does Real-Time Data Processing Works?
- Advantages and Disadvantages of Real-Time Data Processing