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Practical Applications of Spring Batch

Learn the design principles of Spring Batch through 10 practical batch cases, including settlements, CSV uploads, and transitions to dormant status. Rather than memorizing code, focus on “why is it designed this way?” to understand the entire lifecycle of a batch system, including the Job·Step·Chunk structure, ensuring idempotency, failure-handling strategies, and operational know-how.

3 learners are taking this course

Level Intermediate

Course period Unlimited

Java
Java
Spring Boot
Spring Boot
Spring Batch
Spring Batch
data-processing
data-processing
system-design
system-design
Java
Java
Spring Boot
Spring Boot
Spring Batch
Spring Batch
data-processing
data-processing
system-design
system-design

What you will gain after the course

  • Understanding the Criteria for Choosing Between Spring Batch and @Scheduled, and the Job·Step·Chunk Architecture

  • The ability to design systems that safely handle duplicate settlements and failures through idempotency, retry, and skip strategies

  • Establishing an operational strategy for a batch system, including scheduling, restarting, and notification mechanisms

🤔 Have you ever had an experience like this?

  • Even after thoroughly reading the Spring Batch documentation, have you ever been left with only the terminology, without grasping the big picture?

  • Have you ever accidentally run a settlement batch twice, causing the settlement amount to double and breaking out in a cold sweat?

  • Have you ever had the entire process stop because of one malformed CSV row, or, conversely, been flustered when half of it failed but it still ended as ‘successful’?

  • Having initially built it with multithreading in the name of “making it fast,” only to find it slower and more complex.

  • A batch job that ran overnight failed, but no one knew until morning.

Making a batch “run” is easier than making it “safe to rerun and able to resume after failure.” This course tackles that very point head-on through concepts.

📌 First, let’s be clear about the nature of this course

This course focuses primarily on explaining concepts. It does not include hands-on exercises where you follow along and type code line by line.

Now that AI coding is commonplace, AI can write the detailed API usage for you. What truly matters is understanding the concepts needed to judge “how to divide this task into what structure and how to prepare for failures”.

That’s why this course provides a textbook and slide images created from it, using them together to explain structure and design principles.

✨ Why This Course Is Special

🗺️ 1. Master 10 practical cases as ‘one system’

We do not merely list grammar. On a single stage—the back office for operating the fictional shopping mall MarketOn—we build up practical cases step by step, from bulk CSV uploads → settlement → statistics → dormant-account conversion → notifications → archiving → external API integration → search indexing. Each concept remains not as scattered knowledge, but as a map of interconnected cases.

🛡️ 2. 🃏 'Cheat Key' — A Mindset of "Safe to Run Again"

🃏 This course's cheat code

"Don't do everything at once—break it down into smaller parts, and make it safe to run again."

The principles of idempotency and restartability that prevent incidents such as payouts being doubled or data being corrupted — learn through real settlement cases how to layer safeguards, including the cutoff date as a JobParameter, UNIQUE constraints in the DB, and state management (READY→CONFIRMED→PAID). This is the unique strength of this course that sets it apart from the rest.

🎯 3. We teach ‘decision criteria,’ not syntax

When should you choose Retry, Skip, or Fail? This course has a clear answer. "Policies are determined by the business, not the code." Even the same 'DB constraint violation' should be skipped if it causes duplicate notifications, but halted for investigation if it causes duplicate settlements. Learn these decision-making criteria through real-world cases.

🔄 4. The Entire Batch Lifecycle, from Design to Operations

Starting with “When do you use batch?” (design) → case-specific structures (implementation) → Skip/Retry policies (decision-making) → multithreading and partitioning (performance) → scheduling, restarting, monitoring, and failure alerts (operations), it covers the entire lifecycle of batch processing in one continuous flow. It also highlights the latest changes in Spring Batch 5.2–6.x.

🎓 What You’ll Be Able to Do After This Course

  • Determine for yourself which tasks are suitable for batch processing, when @Scheduled is sufficient, and when Spring Batch is needed.

  • Illustrate the big picture of how Jobs, Steps, Chunks, and metadata tables work together.

  • Explain why idempotency is an absolute requirement for settlements, aggregations, and deletions, and how to guarantee it.

  • Distinguish and select Retry, Skip, or Fail according to the nature of the task

  • Approach performance scaling with the principle of "measure first, expand later"

  • Secure the perspective of an operationally viable batch system through restarts, history tracking, and failure alerts.

Recommended for
these people

Who is this course right for?

  • A backend developer with experience in Java and Spring Boot who needs to design batch processing for large volumes of data

  • Developers who have written Spring Batch code but are confused about its design principles and the big picture

  • Lead developer responsible for making architectural decisions and establishing incident response strategies for the batch system

Need to know before starting?

  • Basic syntax of the Java programming language and object-oriented concepts

  • Basic experience using the Spring Boot framework

  • Writing Basic Database (SQL) Queries and Understanding Transactions

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19 lectures ∙ (6hr 44min)

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