Spring Reactive Programming WebFlux —...
December 26, 2025
Spring Batch Processing Tutorial Enterprise Edition (2025 Guide)
Spring Batch is the backbone of enterprise software development, powering large-scale ETL pipelines, financial reporting, compliance jobs, and scheduled data synchronization across mission-critical systems, Spring Batch enables Java teams to process millions of records reliably and efficiently.
This guide explains Spring Batch architecture, chunk processing, job orchestration, retries, parallel execution, scheduling, and production deployment strategies used in real-world enterprise systems.
What Is Spring Batch & Why Enterprises Use It
Spring Batch is a lightweight, robust framework designed specifically for high-volume batch processing. Unlike real-time streaming systems, batch jobs prioritize reliability, consistency, and transaction safety when working with large datasets.
- Handles millions of records reliably
- Chunk based transactional processing
- Built-in retry & skip policies
- Parallel execution support
- Seamless Spring Boot integration
- Ideal for ETL, compliance & scheduled automation
For real-time, non-blocking workloads, teams often complement batch systems with reactive architectures such as Spring WebFlux.
Spring Batch Architecture Overview
Spring Batch follows a layered execution model:
- Job — A complete batch process
- Step — Logical phase inside a job
- Chunk — Transactional unit of processing
- ItemReader — Reads data
- ItemProcessor — Applies business logic
- ItemWriter — Writes output
- JobRepository — Stores execution metadata
- JobLauncher — Triggers execution
Designing reliable batch architectures like this is typically handled by experienced backend engineering teams who specialize in transactional systems, orchestration, and fault tolerance.
Spring Batch Project Setup
<dependency>
<groupId>org.springframework.batch</groupId>
<artifactId>spring-batch-core</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-batch</artifactId>
</dependency>
Spring Boot auto-configures the JobRepository, JobLauncher, and required infrastructure, making Spring Batch production-ready out of the box.
Chunk-Based Processing (Reader → Processor → Writer)
.step("importUsers")
.<User, ProcessedUser>chunk(1000)
.reader(userReader())
.processor(userProcessor())
.writer(userWriter())
.build();
- Reads data in controlled chunks
- One transaction per chunk
- Automatic rollback on failure
- Optimized memory usage
Multi-Step Job Orchestration
@Bean
public Job userJob() {
return jobBuilderFactory.get("userJob")
.start(step1())
.next(step2())
.next(step3())
.build();
}
Parallel Execution & Scaling
taskExecutor.setCorePoolSize(10);
taskExecutor.setMaxPoolSize(20);
Supported strategies:
- Multi-threaded steps
- Partitioned processing
- Remote chunking
- Kafka-backed batch workers
Large-scale parallel batch systems are commonly implemented as part of broader Data Engineering & ETL platforms.
Retries, Skips & Fault Tolerance
.faultTolerant()
.retryLimit(3)
.skipLimit(50)
.retry(SQLException.class)
.skip(ParseException.class)
This ensures batch resilience without manual recovery scripts.
Scheduling Batch Jobs with Spring Boot
@Scheduled(cron="0 0 1 * * ?")
public void runBatch() throws Exception {
jobLauncher.run(userJob(), new JobParameters());
}
Enterprise Deployment Options
- Docker-based batch runners
- Kubernetes CronJobs
- AWS Batch
- Azure WebJobs
- On-prem schedulers
- Microservice batch workers
In enterprise environments, these deployment models are automated and governed using Cloud & DevOps pipelines to ensure reliability, observability, and rollback safety.
See how this approach is applied in real-world systems in our Secure File Transfer ETL Pipeline case study.
Spring Batch Best Practices
- Keep processing idempotent
- Use job parameters
- Externalize configuration
- Tune chunk size
- Prefer stateless processors
- Enable monitoring (Actuator, Prometheus)
