Spring Batch Processing Tutorial Enterprise Edition (2025 Guide)
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)
