Stream Processing vs. Batch Processing vs. Real-time Processing
Published: 2023-02-23 18:14:45

The amount of data created and consumed globally is projected to surpass [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) over the years to 2025. (That’s humongous! One zettabyte is equal to a trillion gigabytes!) And with the exponential growth, developers are expected to engineer solutions to analyze petabytes of data at any given time to derive business value from it.

However, there are many types of data processing today to help. So there’s also the growing question of how to use them best. To better understand their specifics, this post explains the primary difference between batch processing, stream processing, and real-time processing.

##Stream processing

[Stream processing](https://www.simform.com/blog/stream-processing/) is a relatively new data processing method that has become a must-have for modern apps. It collects and analyzes data that is in motion, delivering results to the destination on the fly.

For example, a soft drink company wants to boost brand interest after telecasting a commercial during a sports event. It will feed social media data directly into an analytics system to assess audience response and decide how to enhance brand messaging instantly based on the responses. Here, processing and querying are done continuously.

Thus, stream processing is ideal when events need to be detected immediately and responded to quickly, such as for cybersecurity and fraud detection. If transactional data is stream-processed, fraudulent transactions can be identified instantly and stopped before they are entirely executed.

At times, stream processing entails running a diverse set of tasks in parallel or series (or sometimes in both) for on-the-fly analytics. However, data streams can also be a source for historical data collection. In that case, an additional warehouse can store data to be formatted and further used for analysis or BI.

##Batch processing

It is a relatively traditional approach and the opposite of streaming data. Here, the data is ingested in discrete chunks ((e.g.hourly/daily/weekly) rather than a continuous stream. Thus, a “batch” is a data set or group of data collected within a given time period.

Batch processing collects data in batches, stores it, and then feeds it into an analytics system. For example, end-of-cycle processing such as settling overnight trades, end-of-day or monthly generation of reports, or payroll systems.

Most organizations work with batch [data pipelines](https://dev.to/mpangrazzi/data-pipelines-with-luigi-87d), but there is an increasing appetite for streaming and real-time use cases.

##Real-time processing

The terms “real-time” and “streaming” are often used interchangeably, but they differ slightly.

Real-time processing is about reactions to data. It can guarantee a reaction within tight deadlines. And it can be in a matter of minutes, seconds, or milliseconds, depending on the interest of the business stakeholders and consumers.

For example, executing stock trades in real-time, matching drivers and apps like Lyft, or tracking goods/packages in supply chains.

On the other hand, stream processing is about actions taken on the data. It encompasses continuous computation that happens as data flows through a system.

Below is a quick comparison between the three data processing types and their major differences.

##A Quick Comparison


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nbu279274et080tvu1du.png)


##Summing up

We hope this article gives you a mental model of the differences and what to keep in mind when building [data engineering](https://dev.to/seattledataguy/data-engineering-101-from-batch-processing-to-streaming-g91) systems. For instance, it does not necessarily mean a streaming infrastructure needs to be deployed just because the system is labeled real-time or near real-time. And as for when to use which, it comes down to the project you have on your hands!