Batch vs Stream Processing

While businesses migrate their data from legacy infrastructure to the cloud to enhance security, process compliance across devices, privacy, and data management, there is an interesting debate on how to process the same data. Whether batch or stream processing, it is important to consider the pros and cons of each.

What is the difference between batch and stream processing?

In batch processing, data is collected in groups over a period of time, then fed into the analytics system. The results of this process are not available in real-time. But in stream processing, data is fed into the analytics system as soon as it is available, providing processing results in real-time. Each model applies to different uses in the business, but is one better than the other?

Batch processing

This processing model requires sets of data collected over time before it is fed into the analytics system. While it doesn’t provide results of the processing in real time, it does find important applications in businesses.

If your business handles large volumes of data from legacy systems, and you have projects requiring deeper data analysis, you would adopt batch processing. One challenge you may face when dealing with large volumes of data is that it is not feasible to deliver it in streams. As such, your IT team has to break it down into batches and process it one batch at a time.

Also, some business activities, such as keeping track of revenue, require batch processing. It wouldn’t make a lot of sense to process every sale and provide the results in real-time. It would be much better to collect the sales in batches, possibly at the end of the day, week or month, before computing them.

Stream processing

As for stream processing, data is fed into the analytics system as it is generated. Instead of waiting for data to accumulate in batches, you feed each data point directly into the analytics platform, allowing your teams to produce important insights in real-time. But this processing is not suitable for large volumes of data.

This type of processing is suitable for projects that need speed and nimbleness. For example, if you need to amplify brand interest following a commercial ad during a sporting event, you will need stream processing. You feed social media data directly into the analytics engine to measure audience reaction. From there, you can decide which brand-boosting message to adopt in real-time.

Is one better than the other?

When it comes to data processing, you won’t find a universally superior method. Batch and stream processing each has its strengths and weaknesses. Which one to choose really depends on the project at hand. To remain agile, most companies are moving towards stream processing, but still need batch processing for their large volumes of data.

Flexibility also counts when choosing the processing model to use for your business. Different projects may call for different approaches, hence, require different processing methods. This implies that your teams should have the resources to support batch and stream processing. As long as legacy systems are still in place, batch processing will be an integral feature of your business, even as you strive to move to the cloud and provide feedback in real-time. As such, there is no clear winner between batch and stream processing.

If data is proving to be a challenge for you and your business, partner with us at Helios. We have the expertise and resources to handle all your data, and teach you how to use it to take your business to the next level. If you need help with your data, please contact us today.



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