Data Processing: Batch vs. Stream

Data is the lifeblood of any organization, but if you don’t do something with all those bits and bytes, they won’t be doing anyone any good. They need to be processed.

There are two key approaches to processing data:

1. Batch processing
2. Stream processing (sometimes called real-time processing)

In some circles, you’ll hear the first talked about as being the old way of doing things and the second as the more modern approach. The same sort of language is used when comparing monolithic apps to microservices or on-premise solutions to the cloud.

In reality, things aren’t quite that simple in this case…or in those other cases mentioned. Stream processing isn’t so much a replacement for batch processing as it is a different approach, and it’s not without its challenges.

In this post we’ll consider the idea of batch processing vs. stream processing more broadly, covering things like when to use stream processing and when batch processing in big data might be more appropriate.

What is batch processing?

For a long time, the status quo in the space has been to process data in batches, e.g. nightly, weekly, or after every 1,000 entries. This method is tried and tested, and is still used by many large companies today. But there are a couple of reasons why it’s fallen out of favor:

1. As data gets bigger, with more and more of it being produced by the minute, batch processing fails. Batches need to be processed with exponentially increasing regularity.
2. In 2022, there’s significant emphasis on real-time analysis in the data space. This isn’t possible with batch processing, as data may be out of date before it can be acted on.

Micro-batch processing is one option that emerged as a possible solution to these problems.

What is stream processing?

Historically, stream processing has often been referred to as “real-time processing.” That makes sense, because these terms both refer to the practice of handling data as it’s created.

The “stream” in stream processing refers to the data stream, more accurately capturing the way that actions are taken while the data remains in a stream. Analytics, enrichment, and ingestion are all possible without causing any disruption to that data stream.

Key differences between batch and stream processing

The following ten differences reveal why choosing between these approaches can make or break a business initiative, affecting everything from customer experience to operational costs.

1. Data input and ingestion

Batch processing collects information like gathering all your mail at the end of the day. Organizations accumulate data from various sources throughout a period, then process everything together in one complete operation. This approach works well when you need to see the complete picture before making decisions, like calculating total monthly sales or analyzing quarterly customer trends.

Stream processing handles information as it arrives, like answering phone calls throughout the day. Each piece of data gets attention immediately when it enters the organization.

2. Latency

Batch processing operates on business schedules rather than real-time demands. Your monthly reports might be generated overnight, processing the previous day’s activities to update dashboards that managers review during morning meetings.

Stream processing delivers results within seconds or milliseconds. When someone uses a potentially stolen credit card, fraud detection must happen instantly to prevent unauthorized purchases.

3. Processing frequency

Batch processing follows predictable schedules that align with business rhythms. Reports might run weekly, inventory updates daily, and financial calculations monthly.

Stream processing operates continuously without natural breaks for maintenance or troubleshooting.

4. Data storage

Batch processing works with stable collections of information stored in organized repositories. Your input data doesn’t change during processing.

Stream processing must track ongoing activities across multiple related events.

5. Platform complexity

Batch processing platforms provide straightforward operational models. Organizations define their analysis requirements and wait for results.

Stream processing requires coordinating multiple components that must work together continuously.

6. Scalability

Batch processing scales by adding more computing power to handle larger datasets. Cloud platforms work well because organizations can automatically provision resources for scheduled jobs.

Stream processing requires scaling across distributed infrastructure that handles continuous information flow.

7. Error handling

Batch processing handles problems through systematic retry and recovery mechanisms. If processing fails partway through, organizations can restart from checkpoints.

Stream processing must handle errors while maintaining continuous operation.

8. Data accuracy

Batch processing achieves high accuracy by processing complete datasets under controlled conditions.

Stream processing trades some accuracy for speed and responsiveness.

When to use stream processing vs. batch processing?

The choice between stream and batch processing depends on your business requirements, industry constraints, and competitive landscape.

Choose stream processing when

• Immediate action creates competitive advantage or prevents losses
• Customer experience depends on instant responsiveness
• Operational monitoring requires instant alerts
• Market conditions change rapidly

Choose batch processing when

• Accuracy and completeness matter more than speed
• Analysis requires complete datasets
• Cost optimization is a priority
• Complex calculations need stable data

Industry-specific use cases

• Healthcare: Patient monitoring uses stream processing, while medical research uses batch processing.
• Retail: Real-time inventory updates rely on stream processing, while sales forecasting uses batch processing.
• Financial services: Fraud detection requires stream processing, while regulatory reporting uses batch processing.
• Manufacturing: Equipment monitoring uses stream processing, while production planning uses batch processing.
• Media and entertainment: Live content moderation uses stream processing, while audience analysis uses batch processing.

Many organizations implement hybrid approaches, using stream processing for operational needs and batch processing for analytical insights.

Conclusion

There’s a tendency to pit these two methods – batch processing vs. stream processing – against each other as if one is going to come out as the perfect solution. In reality, when to use stream processing or batch processing in big data is far more likely to come down to the project you have on your hands: stream processing for those that require instant feedback and batch processing for in-depth analysis of data that isn’t so time-sensitive.