Difference between Batch processing and Real-time Processing
Following are the differences between Batch processing and Real-time processing:
| Characteristics | Batch Processing | Real-time Processing |
|---|---|---|
| Job Frequency | It has infrequent jobs that produce results once the job has finished running | It has continuously running jobs that produce constant results |
| Processing Speed | Slower processing of data in chunks after accumulation | Immediate processing of individual data points |
| Job Control | Batch processes can be postponed or halted whenever required | Real-time processes need to respond instantly |
| Latency | High latency performance (minutes or hours) | Low latency performance (milliseconds to seconds) |
| Complexity | Less complex | More complex |
| Scalability | More scalable and cost effective | Less scalable and less cost effective |
| Interactivity | Not interactive enough | Highly interactive |
| Data Sources | Data sources are databases, APIs, static files | Data sources are message queues, data points |
| Opposite | It is the opposite of real-time processing | It is the opposite of batch processing |
| Data Collection | Collects data over time and sends it for processing once collected | Continuously collects data and processes it fast, piece by piece |
What is the difference between batch processing and real-time processing?
In this article, we will learn about two fundamental methods that govern the flow of information and understand how data gets processed in the digital world. We start with simple definitions of batch processing and real-time processing, and gradually cover the unique characteristics and differences.
Table of Content
- Data processing in Data Engineering
- Batch Processing in Data Engineering
- Real-time Processing
- Difference between Batch processing and Real-time Processing
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