Definition: Batch processing involves collecting data over a set period, storing it, and then processing it all at once in “batches” at regular intervals. This approach is ideal when you don’t need instant or up-to-the-minute data.
How It Works:
- Data Collection: Data is collected and stored over time. For example, data could be accumulated throughout the day.
- Batch Execution: At the end of a given interval (such as daily, weekly, or hourly), a batch job runs, processing all the accumulated data.
- Data Availability: Processed data is typically available after the batch job completes, so there is a delay between data collection and availability.
Key Characteristics:
- Latency: High latency, as data is processed in bulk after collection.
- Efficiency: Efficient for large volumes of data that don’t require immediate results.
- Cost: Cost-effective because processing happens less frequently, reducing compute costs.
- Throughput: High throughput, as large volumes of data are processed together.
Examples of Batch Processing Use Cases:
- Financial Reporting: Running daily or monthly reports on transactions.
- ETL in Data Warehousing: Loading data into a data warehouse in periodic batches, like overnight or on weekends.
- Data Analytics: Analyzing user behavior based on past data (e.g., weekly customer engagement trends).
- Backups and Archival: Scheduled data backups or archival for historical analysis.
Advantages of Batch Processing:
- Scalability: Can handle large data volumes efficiently.
- Cost-Effectiveness: Batch processing can be more economical since it often runs during off-peak hours or at a reduced frequency.
- Simplicity: Easier to implement and monitor, especially for periodic data processing.
Limitations of Batch Processing:
- High Latency: Results are not available in real time, making it unsuitable for applications that require immediate data insights.
- Resource Intensive: When dealing with very large data sets, the system might experience high resource usage during batch jobs.