Batch vs Stream Processing PDF

206 Batch Processing vs Stream Processing ** Batch processing and stream processing are two methods used for processing large volumes of data, each suited for different scenarios and data processing needs. ** ~ [Batch Processing vs...

206 Batch Processing vs Stream Processing ** Batch processing and stream processing are two methods used for processing large volumes of data, each suited for different scenarios and data processing needs. ** ~ [Batch Processing vs Stream Processing] ~ Batch Processing ** Definition: Batch processing refers to processing data in large, discrete blocks (batches) at ** * scheduled intervals or after accumulating a certain amount of data. * ** Characteristics: ** *** Delayed Processing: Data is collected over a period and processed all at once. * ** * * *** High Throughput: Efficient for processing large volumes of data where immediate * ** *** *** * ~~ ~~ action is not necessary. * ** Example: Payroll processing in a company. Salary calculations are done at the end of ** *** *** each pay period (e.g., monthly). All employee data over the month is processed in one * * * large batch to calculate salaries, taxes, and other deductions. * *** *** ** Pros: ** ** Resource Efficient: Can be more resource-efficient as the system can optimize for large ** * data volumes. *  ** Simplicity: Often simpler to implement and maintain than stream processing systems. ** *** *** ~~ ~~ ** Cons: ** ** Delay in Insights: Not suitable for scenarios requiring real-time data processing and ** *** *** ~~ ~~ action. ** Inflexibility: Less flexible in handling real-time data or immediate changes. ** * * ~~ ~~ ~~ ~~ ~~ ~~ Stream Processing ** Definition: Stream processing involves continuously processing data in real-time as it ** *** *** *** *** arrives. ** Characteristics: ** ** Immediate Processing: Data is processed immediately as it is generated or received. ** *** *** ** Suitable for Real-Time Applications: Ideal for applications that require instantaneous ** * * *** data processing and decision-making. *** ** Example: Fraud detection in credit card transactions. Each transaction is immediately ** *** *** analyzed in real-time for suspicious patterns. If a transaction is flagged as fraudulent, *** *** the system can trigger an alert and take action immediately. ** Pros: ** ** Real-Time Analysis: Enables immediate insights and actions. ** *** *** ** Dynamic Data Handling: More adaptable to changing data and conditions. ** *** *** ** Cons: ** ** Complexity: Generally more complex to implement and manage than batch processing. ** *** *** ** Resource Intensive: Can require significant resources to process data as it streams. ** *** *** Key Differences * * ** Data Handling: **  ** Batch processing handles data in large chunks after accumulating it over time, ** *** *** *** *** * while stream processing handles data continuously and in real-time. * ** ** *** *** *** *** ** Timeliness: ** ** Batch processing is suited for scenarios where there's no immediate need for data ** ~~ ~~ processing, * whereas stream processing is used when immediate action is required based on the * ** ** *** *** incoming data. ** Complexity and Resources: ** ** Stream processing is generally more complex and resource-intensive, catering to real- ** *** *** *** *** *** time data,*** * compared to the more straightforward and scheduled nature of batch processing. * *** *** *** *** ** ** Conclusion The choice between batch and stream processing depends on specific application * requirements. * ** Batch processing is suitable for large-scale data processing tasks that don't require ** *** *** ~~ immediate action, like financial reporting. ~~ *** *** ** Stream processing is essential for real-time applications, like monitoring systems or ** *** *** *** *** *** real-time analytics, where immediate data processing and quick decision-making are *** *** *** *** *** crucial.