Daniel Greenberg

In today’s Big Data landscape, there are two primary ways of processing data: batch processing and stream processing.

Both methods offer unique advantages and disadvantages, depending on your use case. Here’s a quick overview of both, including the pros and cons of each method:

 

Batch Processing: Large, Complex Data Analysis

With batch processing, data is collected in batches and then fed into an analytics system. A “batch” is a group of data points collected within a given time period.

Unlike stream processing, batch processing does not immediately feed data into an analytics system, so results are not available in real-time. With batch processing, some type of storage is required to load the data, such as a database or a file system.

Batch processing is ideal for very large data sets and projects that involve deeper data analysis. The method is not as desirable for projects that involve speed or real-time results. Additionally, many legacy systems only support batch processing.

This often forces teams to use batch processing during a cloud data migration involving older mainframes and servers. In terms of performance, batch processing is also optimal when the data has already been collected.

Batch Processing Example: Each day, a retailer keeps track of overall revenue across all stores. Instead of processing every purchase in real-time, the retailer processes the batches of each store’s daily revenue totals at the end of the day.

 

Stream Processing: Speed and Real-Time Analytics

With stream processing, data is fed into an analytics system piece-by-piece as soon as it is generated. Instead of processing a batch of data over time, stream processing feeds each data point or “micro-batch” directly into an analytics platform. This allows teams to produce key insights in near real-time.

Stream processing is ideal for projects that require speed and nimbleness. The method is less relevant for projects with high data volumes or deep data analysis.

When coupled with platforms such as Apache Kafka, Apache Flink, Apache Storm, or Apache Samza, stream processing quickly generates key insights, so teams can make decisions quickly and efficiently. Stream processing is also primed for non-stop data sources, along with fraud detection, and other features that require near-instant reactions.

Stream Processing Example: A soda company wants to amplify brand interest after airing a commercial during a sporting event. The company feeds social media data directly into an analytics system to measure audience response and decide how to boost brand messaging in real-time.

 

Batch vs. Stream Processing: What to Use?

With data processing, there is no universally superior method. Batch and stream processing each have strengths and weaknesses, depending on your project. In an effort to stay agile, companies continue to gravitate toward stream processing.

But batch processing is still widely used and will be so long as legacy systems remain an integral component of the data ecosystem.

When it comes to data processing, flexibility is the most important factor for data teams. Different projects call for different approaches. Teams must have the wherewithal to find optimal solutions for each use case.

There is no clear winner in a comparison between batch and stream processing. The winners are the teams that can work with both.

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