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Real-Time Data vs. Streaming Data

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The concept of big data has recently gathered a lot of attention with the surge in technologies related to artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT). Organizations today demand data precision and accuracy for operations and analyses that have to be performed in the blink of an eye — as datasets become too large or complex to be handled or administered through traditional technologies. To remain competitive, organizations require a real-time and diverse flow of data with seamless transmission anywhere in the world. When it comes to data flow, the terms real-time and streaming data are often misunderstood and used interchangeably. Let’s go through what makes these two concepts distinct from one another yet possible to combine in certain solutions. 

What is the difference between real-time and streaming data?

Real-time data latency is measured in micro to milliseconds and does not allow any data transmission delays. Any hindrance in the data flow can cause the entire system to collapse. While real-time data latency is calculated in milliseconds, near real-time data latency can be extended to seconds. Near-real-time data can be used for operational intelligence or event-driven systems.

Streaming data, on the other hand, is constantly acquiring and transmitting data while being updated and is always accessible. In the simplest terms, streaming data is the systematic flow of data created by diverse sources. As a result, streaming data requires a collection and analysis system that has efficient throughput and dependability. As streaming data is never funneled to relatively long-term storage, any disruption in such systems can frequently result in data deletion. This type of flowing data must be tracked consecutively and progressively for a range of insights such as correlations, filtration, sampling, and aggregations. Streaming data examples include astronomical observations, climate observation systems, and earth-sensing satellites.

Real-time data is available as it is created or acquired to allow businesses to act when the events occur and has a concept relating to current practical restrictions, and a definition governed by an initial warning to prevent the system from malfunctioning. Whereas streaming data specifies uninterrupted ingestion of data without any response time requirement. Streaming data is an enterprise requirement but is not always able to be processed in real-time. This implies that a system may be created using data ingestion capabilities as well as streaming pipelines to process the data as it flows consistently, but the latency is higher than what is required for a real-time process.


Both real-time and streaming data have been vital to AI, IoT, and ML operations. With the development in cloud and edge computing where immediate and continuous data transmission is a critical requirement, a real-time and streaming data system provides enterprises with accessibility across several aspects of their businesses such as server activity, or geo-location of devices, people, and physical goods and allows them to adapt effectively to new situations.

Enterprises can take advantage of Macrometa's ready-to-go industry solutions that are powered by the real-time stream processing of millions of events per second in regional and global environments.

Related reading:

Complex Event Processing

How Stream Processing Can Enable Data Enrichment


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