Batch Processing vs. Stream Processing: 8 Key Differences You Should Know
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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:
- Batch processing, or
- 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:
- 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.
- 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 This isn’t possible with batch processing, as data may be out of date before it can be acted on.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 micro-batch processing? Well, as the name suggests, it involves processing very small batches of data, often in quick succession. In some cases, micro-batches might be as tiny as a few minutes (or even seconds) worth of data.
But, as we’ll see below, there are some cases in which that’s still not fast enough…
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.
Real-time processing, however, sort of implies that data is taken somewhere to be dealt with as it arrives in real time. In fact, the process we’re talking about here isn’t that invasive.
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.
| Related: Why you need to prioritize data quality when migrating to stream processing. |
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. Each distinction carries real consequences for organizations trying to leverage their data effectively.
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. Customer clicks on a website, sensor readings from equipment, or transaction records all get processed the moment they occur.
The fundamental difference lies in timing and completeness. Batch processing waits for all the information to arrive before starting work, while stream processing acts on each piece of information individually as it becomes available. This choice affects how quickly your organization can respond to changes and what types of insights you can generate.
Organizations must decide whether they need immediate responses to individual events or whether they can wait to process information in groups for more complete analysis.
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. Results arrive hours or days after events occur, which works well for strategic planning and historical analysis.
This delayed approach allows organizations to optimize for accuracy and thoroughness over speed. You can process large amounts of information efficiently, taking advantage of powerful computing resources during off-peak hours when they’re less expensive and more available.
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. Real-time systems must react faster than human decision-making, identifying problems and opportunities while they’re still actionable.
However, this speed comes with trade-offs. Organizations must invest in infrastructure that responds immediately and accept that some decisions will be made with incomplete information for the sake of timely action.
3. Processing frequency
Batch processing follows predictable schedules that align with business rhythms. Reports might run weekly, inventory updates daily, and financial calculations monthly. This regular timing allows organizations to plan maintenance, coordinate between departments, and allocate resources effectively.
The scheduled nature provides flexibility when things don’t go as planned. If data sources are delayed or processing takes longer than expected, organizations can extend deadlines or add more resources without disrupting ongoing operations. Problems can be fixed and jobs rerun during the next scheduled cycle.
Stream processing operates continuously without natural breaks for maintenance or troubleshooting. Information flows constantly, requiring organizations to handle varying workloads gracefully, scaling resources up during busy periods and down during quiet times.
This continuous operation creates different management challenges. Organizations need round-the-clock monitoring and must be prepared to handle problems while operations continue running.
4. Data storage
Batch processing works with stable collections of information stored in organized repositories. Your input data doesn’t change during processing, which simplifies quality control and allows reprocessing if needed. Organizations can take time to verify accuracy and completeness before generating results.
This stability enables sophisticated optimization techniques. Data can be organized efficiently for fast analysis, compressed for storage savings, and structured to support complex business questions.
Stream processing must track ongoing activities across multiple related events. Understanding customer sessions requires following their journey across many interactions. Detecting fraud patterns needs counting behaviors over time periods. This creates complexity around maintaining context and ensuring accuracy.
Managing this ongoing state becomes a significant organizational challenge. Teams must decide how long to remember information, how to handle late-arriving data, and how to maintain accuracy while processing continues.
5. Platform complexity
Batch processing platforms provide straightforward operational models. Organizations define their analysis requirements, submit processing jobs, and wait for results. The underlying technology handles resource allocation and problem recovery automatically. Troubleshooting involves examining what happened after processing completes.
Resource planning stays manageable because batch jobs have predictable requirements. Organizations can estimate computing needs based on historical patterns and scale up for known busy periods. Cost optimization involves scheduling expensive operations during off-peak hours.
Stream processing requires coordinating multiple components that must work together continuously. Message handling manages information flow. Processing engines handle computation and memory. Storage destinations receive results. Each component scales differently and fails in unique ways.
This distributed approach multiplies operational complexity. Organizations need monitoring for delays, load balancing, and service coordination. Problems can cascade between components, requiring sophisticated isolation and recovery strategies.
6. Scalability
Batch processing scales by adding more computing power to handle larger datasets. Organizations can add more machines or upgrade to more powerful hardware to process bigger volumes of information. The predictable nature of batch work makes capacity planning straightforward since you know the size of your datasets.
Cloud platforms work well for batch processing because organizations can automatically provision resources for scheduled jobs, then shut them down to minimize costs. This elasticity matches the burst-heavy nature of batch operations perfectly.
Stream processing requires scaling across distributed infrastructure that handles continuous information flow. Your processing capacity depends on how you’ve organized your data streams, which must be planned carefully since reorganizing later requires significant changes.
Resource management becomes more complex because stream processing loads vary unpredictably. Organizations need enough capacity for peak periods but want to avoid paying for unused resources during quiet times.
7. Error handling
Batch processing handles problems through systematic retry and recovery mechanisms. If processing fails partway through, organizations can restart from checkpoints rather than beginning again. The stable nature of input data ensures that retries produce consistent results.
Problem isolation remains manageable because failed batch jobs only affect scheduled reports or planned analyses. Organizations have time to investigate causes and fix issues before the next processing cycle. Data quality problems can be identified and corrected systematically. Data observability platforms like Monte Carlo help organizations monitor their batch processing pipelines, detecting anomalies and quality issues before they impact business decisions.
Stream processing must handle errors while maintaining continuous operation. Messages that cause processing failures need automatic routing to holding areas. Service outages require backup systems and retry logic to avoid overwhelming recovering components.
The continuous nature means errors impact real-time operations immediately. A problem in your stream processing logic affects all subsequent events until you deploy a fix. Error recovery becomes more complex because organizations must decide whether to replay missed information or accept some data loss to minimize downtime. Real-time data quality monitoring tools like Monte Carlo become essential for detecting issues as they occur in streaming operations.
8. Data accuracy
Batch processing achieves high accuracy by processing complete datasets under controlled conditions. Organizations can run thorough quality checks, validate business rules, and ensure consistency across all information. The all-or-nothing nature of batch jobs means that either all transformations succeed or none do.
Perfect processing comes naturally with batch jobs because input data doesn’t change during analysis. Organizations can implement consistent transformations that produce identical results regardless of how many times they run. This reliability makes batch processing ideal for financial calculations and regulatory reporting where accuracy is essential. Data quality platforms like Monte Carlo help organizations validate information accuracy across complete datasets before results reach decision-makers.
Stream processing trades some accuracy for speed and responsiveness. Perfect consistency is possible but requires complex implementation. Many stream processing applications accept minor inconsistencies to simplify operations and improve response times.
The accuracy trade-offs become apparent in time-based calculations. Late-arriving information can change results after decisions have already been made. Organizations must decide whether to accept slightly inaccurate results for timely action or implement complex logic to handle delayed information.
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. Knowing when each approach delivers maximum value helps organizations make informed technology decisions that align with their strategic objectives.
Choose stream processing when
Immediate action creates competitive advantage or prevents losses
Financial services use stream processing for fraud detection because every second of delay increases the risk of unauthorized transactions. E-commerce platforms implement real-time recommendation engines to influence purchasing decisions while customers actively browse products.
Customer experience depends on instant responsiveness
Ride-sharing companies like Uber rely on stream processing to match drivers with passengers, calculate dynamic pricing, and provide real-time location updates. Gaming platforms use stream processing to enable multiplayer experiences where delays would ruin the user experience.
Operational monitoring requires instant alerts
Manufacturing companies use stream processing to monitor equipment sensors and prevent costly breakdowns. Healthcare organizations implement real-time patient monitoring systems where delayed alerts could have life-or-death consequences.
Market conditions change rapidly
Trading firms use stream processing to react to market data within microseconds. Social media platforms depend on real-time processing to detect trending topics and adjust content algorithms as conversations develop.
Choose batch processing when
Accuracy and completeness matter more than speed
Financial institutions use batch processing for regulatory reporting where accuracy is legally required and deadlines allow for thorough validation. Insurance companies process claims in batches to ensure all relevant information is considered before making coverage decisions.
Analysis requires complete datasets
Retail companies use batch processing for inventory planning and demand forecasting, which need historical sales patterns across entire product catalogs. Media companies analyze complete viewing histories to improve content recommendations and programming decisions.
Cost optimization is a priority
Organizations with predictable reporting needs use batch processing to take advantage of off-peak computing rates. Non-profit organizations often choose batch processing for donor analysis and campaign planning to minimize technology costs.
Complex calculations need stable data
Research institutions use batch processing for scientific analysis where data consistency during computation is critical. Marketing teams use batch processing for customer segmentation and campaign performance analysis that requires stable customer data.
Industry-specific use cases
Healthcare
Patient monitoring and emergency alerts use stream processing, while medical research and population health studies use batch processing for analyzing large patient datasets.
Retail
Real-time inventory updates and personalized shopping experiences rely on stream processing, while sales forecasting and supply chain planning use batch processing for strategic decisions.
Financial services
Fraud detection and algorithmic trading require stream processing, while regulatory reporting and risk analysis use batch processing for accuracy and compliance.
Manufacturing
Equipment monitoring and quality control use stream processing to prevent defects, while production planning and supply chain optimization use batch processing for operational efficiency.
Media and entertainment
Live content moderation and real-time viewer engagement use stream processing, while audience analysis and content strategy development use batch processing.
Many organizations implement hybrid approaches, using stream processing for operational needs and batch processing for analytical insights. The key is matching your processing approach to your business requirements rather than choosing based on technology preferences.
Conclusion
There’s a tendency to pit these two methods – batch processing vs. stream processing, a fight to the death, only one can leave the ring! – against each other as if one is going to come out as the perfect solution. But that’s really not the way to look at this comparison.
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, though possibly shallower, feedback and batch processing for in-depth analysis of data that isn’t so time-sensitive.
We’ve not only seen above that there’s a place in data for both of these solutions, but that micro-batch processing can (just about) function as a bridge between the two. Hopefully, armed with the knowledge above, you can now figure out which one works for you.
Still grappling with data quality across batch processing, micro-batch processing, and stream processing pipelines? Data observability can help. Reach out to us by selecting a time in the form below.
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