Difference between Batch Processing and Stream Processing

Today, an immense amount of data is generated, which needs to be managed properly for the efficient functioning of any business organization. Two clear ways of dealing with data are the batch and stream processes. Even though both methods are designed to handle data, there are significant differences in terms of working, application, and advantages. To make the right decision for optimizing the data flow, let’s discuss the definitions of batch processing and stream processing.

What is Batch Processing?

Batch processing refers to the processing of a high volume of data in a batch within a specific time span. It processes large volumes of data all at once. Batch processing is used when data size is known and finite. It takes a little longer time to process data. It requires dedicated staff to handle issues. A batch processor processes data in multiple passes. When data is collected over time and similar data batched/grouped together then in that case batch processing is used. 

Challenges With Batch processing

• Debugging these systems is difficult as it requires dedicated professionals to fix the error.
• Software and training require high expenses initially just to understand batch scheduling, triggering, notification, etc.

Advantages of Batch Processing

• Efficiency in Handling Large Volumes: Batch processing is very efficient when handling big volumes of data because it combines the data and process it at once.
• Reduced Costs: As the processing is in mass, it isn’t very intensive and in some cases can be done outside the business hours and many a times saves the expenses.
• Simplified Error Handling: Batch processing errors are also easy to correct since the data is processed as a batch and an audit performed.

Disadvantages of Batch Processing

• Delayed Results: I want to note that this approach is good only for those tasks where the processing is done with a considerable time delay.
• Inflexibility: When a particular batch job is initiated, it becomes a bit difficult to introduce an alteration or provide means to process the new inputs until the current batch has been processed.

What is Stream Processing?

Stream processing refers to processing of continuous stream of data immediately as it is produced. It analyzes streaming data in real time. Stream processing is used when the data size is unknown and infinite and continuous. It takes few seconds or milliseconds to process data. In stream processing data output rate is as fast as data input rate. Stream processor processes data in few passes. When data stream is continuous and requires immediate response then in that case stream processing is used.

Challenges with Stream processing

• Data input rate and output rate sometimes creates a problem.
• Cope with huge amount of data and immediate response.

Advantages of Stream Processing

• Real-Time Processing: Real time processing is made possible by stream processing, which outputs both results and actions.
• Continuous Data Handling: The real-time processing is ideal for the set-up where there is a constant stream of data that need to be analyzed as soon as possible.
• Scalability: The variability of data flooding can be managed in stream processing systems, thus making them effective for large scale data systems.

Disadvantages of Stream Processing

• Complexity: Stream processing systems, in its totality, is a complex area to implement and manage thus needs special skills.
• Higher Costs: Real-time processing requires more computer power thus be expensive as compared to batch processing.

Difference Between Batch Processing and Stream processing

The main differences between the two are:

• Data Processing Approach: Batch processing involves processing large volumes of data at once in batches or groups. The data is collected and processed offline, often on a schedule or at regular intervals. Stream processing, on the other hand, involves processing data in real-time as it is generated or ingested into the system. The data is processed as a continuous stream, with results generated in near real-time.
• Data Latency: Batch processing is typically slower than stream processing since the data is processed in batches, which can take some time. Stream processing, on the other hand, provides real-time results with low latency, making it suitable for applications that require immediate responses.
• Data Volume: Batch processing is suitable for processing large volumes of data, as it can be processed in batches, making it easier to manage and optimize. Stream processing, on the other hand, is designed to handle high volumes of data, which is processed in real-time.
• Processing Complexity: Batch processing is generally less complex than stream processing since the data is processed offline and in batches. Stream processing is more complex since it requires processing data in real-time, which can be challenging, especially for complex applications.
• Processing Use Cases: Batch processing is well-suited for use cases such as data warehousing, data mining, and data analytics, which involve processing large volumes of historical data. Stream processing is suitable for use cases such as real-time monitoring, fraud detection, and IoT applications, which require real-time processing of data as it is generated.

Conclusion

In conclusion, batch processing is most suitable where delay is acceptable and large amount of data needs to processed. On the other hand, stream processing is used in the situations where real time data analysis is of paramount significance. As it has been seen, both the approaches have their own advantages and disadvantages, hence, the following conditions should be fulfilled when opting for either of the methods to fit your prerequisites of the data processing.