Spring Batch는 대량 데이터를 어떻게 처리할까요?

When operating a service, there are many high-volume tasks that do not require real-time processing. Typical examples include running daily early morning settlements, migrating old data, or aggregating statistical data all at once. Spring Batch is the framework used to handle these tasks reliably. To use a simple analogy, it is similar to not washing clothes one pair at a time, but gathering them in a basket and running the washing machine all at once. Similarly, in software, collecting data in fixed units for reading, processing, and storage allows for more efficient and stable processing. In this video, we summarize the core structure of Spring Batch. We explain the workflow through practical application, covering Job, Step, JobLauncher, JobRepository, ItemReader, ItemProcessor, ItemWriter, chunk-oriented processing, tasklet methods, metadata tables, restart after failure, Skip, Retry, and scheduler integration. 📌 Recommended for: ✅ Backend developers learning Spring Batch for the first time ✅ Those curious about the principles of bulk data processing and chunk-oriented processing ✅ Those who need to design batch jobs such as settlement, statistics, and data migration ✅ Those who want to learn strategies to prevent batch failures, restarts, and duplicate execution #SpringBatch #SpringBatch #BulkDataProcessing #BackendDevelopment #SpringBoot