Difference between Batch Processing and Stream Processing

Prerequisite – Types of Operating Systems 1. Batch Processing : Batch processing refers to processing of high volume of data in batch within a specific time span. It processes large volume of data all at once. Batch processing is used when data size is known and finite. It takes little longer time to processes data. It requires dedicated staffs to handle issues. Batch processor processes data in multiple passes. When data is collected overtime and similar data batched/grouped together then in that case batch processing is used. Challenges with Batch processing :

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

2. 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.

Batch processing and stream processing are two different approaches to processing large volumes of data, each with its own advantages and disadvantages. 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.

In summary, batch processing is better suited for processing large volumes of historical data in batches, while stream processing is better suited for real-time processing of high volumes of data.

Difference between Batch Processing and Stream processing :

S.No.	BATCH PROCESSING	STREAM PROCESSING
01.	Batch processing refers to processing of high volume of data in batch within a specific time span.	Stream processing refers to processing of continuous stream of data immediately as it is produced.
02.	Batch processing processes large volume of data all at once.	Stream processing analyzes streaming data in real time.
04.	In Batch processing data size is known and finite.	In Stream processing data size is unknown and infinite in advance.
05.	In Batch processing the data is processes in multiple passes.	In stream processing generally data is processed in few passes.
06.	Batch processor takes longer time to processes data.	Stream processor takes few seconds or milliseconds to process data.
07.	In batch processing the input graph is static.	In stream processing the input graph is dynamic.
08.	In this processing the data is analyzed on a snapshot.	In this processing the data is analyzed on continuous.
09.	In batch processing the response is provided after job completion.	In stream processing the response is provided immediately.
10.	Examples are distributed programming platforms like MapReduce, Spark, GraphX etc.	Examples are programming platforms like spark streaming and S4 (Simple Scalable Streaming System) etc.
11.	Batch processing is used in payroll and billing system, food processing system etc.	Stream processing is used in stock market, e-commerce transactions, social media etc.
12	Processes data in batches or sets, typically stored in a database or file system.	Processes data in real-time, as it is generated or received from a source.
13	Processes data in discrete, finite batches or jobs.	Processes data continuously and incrementally.