concept#Data#Analytics#Batch Processing#Efficiency

Batch Processing

Batch processing is a process where a group of tasks or data is processed in a single batch.

Batch processing enables the automatic processing of large volumes of data or tasks at scheduled times.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeTechnical
Organizational maturityAdvanced

Technical context

Integrations

Data analysis toolsReporting systemsCloud services

Principles & goals

Principles

Automation of repetitive tasksEfficient use of resourcesError reduction through batch processing

Value stream stage

Build

Organizational level

Team, Domain, Enterprise

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Data loss in case of batch errors
Dependency on batch schedules
High error rate with large batch sizes

Best practices

Regular review of batch performance
Optimization of batch sizes
Ensure secure data integration

I/O & resources

Inputs

Prepared data sources
Batch processing schedule
User data.

Outputs

Processed results
Reports
Updated databases

Resources

Description

Batch processing enables the automatic processing of large volumes of data or tasks at scheduled times. This method is ideal for time-intensive or repetitive processes, ensuring efficiency and accuracy in data processing.

✔Benefits

Increased processing speed
Optimization of resource utilization
Cost efficiency for large volumes of data

✖Limitations

Not suitable for real-time processing
Delays during batch processing
Complexity of troubleshooting

Trade-offs

Metrics

Throughput Rate
The number of processed jobs per unit of time.
Processing Time
The time taken to process a batch of jobs.
Error Rate
The percentage of jobs that are erroneous during batch processing.

Examples & implementations

Processing Customer Orders

Batch processing is used for compiling and processing customer orders.

Automated Monthly Reports

Monthly reports are automatically generated through batch processing.

Data Migration to Cloud

Batch processing enables efficient migration of user data to the cloud.

Implementation steps

Determine processing requirements

Schedule batch processing

Review and test batch jobs

⚠️ Technical debt & bottlenecks

Technical debt

Outdated batch software
Poor documentation
Insufficient test coverage

Known bottlenecks

Delays due to batch processingComplexity of data integrationDependency on batch agreements

Misuse examples

Batch processing for real-time applications
Insufficient error handling
Overloaded batch jobs

Typical traps

Neglecting data validation
Insufficient testing before going live
Lack of documentation for batch processes

Required skills

Knowledge of databasesExperience with batch processesFundamentals of data analysis

Architectural drivers

Need for efficiency increaseIntegration into existing systemsDemand for automation

Constraints

• Minimum hardware requirements
• Compliance with data protection regulations
• Define maximum batch size