Spring Boot Microservices: A Complete Guide from a Software Engineer Who Built 20+ Enterprise Applications

Expert Insight by

Bhuvaneshwari Pothula

Software Engineer, Edward Jones

Enterprise Software Engineering / Financial ServicesLinkedIn

Bhuvaneshwari is a Software Engineer who delivered 20+ enterprise-grade microservices-based applications at Edward Jones. She architected event-driven systems with Apache Kafka processing 500K+ daily transactions, built CI/CD pipelines with 99% deployment success, and led an Enterprise Document Automation MVP that eliminated 1,200+ hours of manual work annually. She holds a BS in Computer Science from the University of Central Missouri and is currently pursuing an MS in Artificial Intelligence at the University of the Cumberlands.

Verified Expert

Building microservices at Edward Jones — a financial services firm managing over $2 trillion in client assets — exposed a reality that no tutorial prepares you for. Compliance requirements shape your architecture. Audit trails aren't optional. And the cost of downtime in financial transactions is measured in regulatory fines, not just lost revenue.

After delivering 20+ enterprise-grade microservices, the lessons that matter most aren't about Spring Boot annotations. They're about the decisions you make before writing a single line of code: how you decompose services, how they communicate, how you deploy them safely, and how you handle the security and compliance requirements that regulated industries demand.

Most Spring Boot microservices tutorials start with a "Hello World" service and a REST endpoint. That's the easy part. The hard part is everything that comes after: how services find each other, how they handle failure, how you deploy 20 of them without breaking production, and how you ensure every transaction is auditable.

At Edward Jones, a "microservice" wasn't just a Spring Boot application with a REST controller. Each service had:

• Its own database schema — no shared databases between services
• Health checks and readiness probes — Kubernetes needed to know when a service was ready
• Centralized logging — every request traceable across service boundaries
• Spring Security with JWT — every inter-service call authenticated and authorized
• Circuit breakers — one failing service couldn't cascade and take down the system

The Spring Boot ecosystem makes this manageable. Auto-configuration handles most setup. Spring Cloud provides service discovery (Eureka), API gateway (Spring Cloud Gateway), circuit breakers (Resilience4J), and distributed tracing. Spring Security integrates JWT authentication with minimal code. And Spring Kafka provides first-class Apache Kafka integration for event-driven communication.

The architecture that most tutorials show — a few boxes connected by arrows — doesn't capture the infrastructure that enterprise microservices actually need. Here's what a real Spring Boot microservices architecture looks like in a regulated financial services environment.

The layers of a production microservices architecture:

1. API Gateway Layer — Spring Cloud Gateway routes external traffic, handles rate limiting, and enforces authentication before requests reach any service. In financial services, this is also where TLS termination and request logging happen for audit compliance.

2. Service Layer — Individual Spring Boot microservices, each with its own responsibility. At Edward Jones, these included a document processing service, a compliance review service, a notification service, a customer request service integrated with Pega BPM, and several data services backed by Oracle and MySQL.

3. Communication Layer — Apache Kafka for asynchronous event-driven communication (processing 500K+ daily transactions) and REST/OpenFeign for synchronous service-to-service calls. The choice between them depends on whether the caller needs an immediate response.

4. Data Layer — Each service owns its data. Some used Oracle (for transactional compliance data), others MySQL (for operational data), and MongoDB (for document metadata). No service directly accessed another service's database.

5. Infrastructure Layer — Docker containers orchestrated by Kubernetes on AWS. Jenkins pipelines for CI/CD. Centralized logging and monitoring for observability across all services.

The architecture evolved over time. The first version was a modular monolith — a single Spring Boot application with clearly separated packages. Only after identifying which modules needed independent scaling and deployment did the team decompose into microservices. This monolith-first approach prevented over-engineering service boundaries before understanding the domain.

Choosing how microservices communicate is the decision with the most architectural impact. At Edward Jones, two patterns handled 95% of inter-service communication:

Synchronous: REST with Spring Cloud OpenFeign

For request-response flows — where one service needs data from another and must wait for the response — OpenFeign provides a declarative REST client that feels like calling a local method:

@FeignClient(name = "compliance-service")
public interface ComplianceClient {
    @GetMapping("/api/v1/rules/{documentType}")
    List<ComplianceRule> getRules(@PathVariable String documentType);
}

When to use REST:

• User-facing requests that need immediate responses
• Data lookups across services (fetching compliance rules, user profiles)
• Health checks and service status queries

The risk: Synchronous calls create temporal coupling. If the compliance service is down, the document service hangs. Circuit breakers (Resilience4J) mitigate this by failing fast and returning fallback responses.

Asynchronous: Apache Kafka for Event-Driven Communication

For high-throughput, fire-and-forget flows — where a service publishes an event and doesn't need an immediate response — Apache Kafka handles the heavy lifting:

@Service
public class TransactionEventPublisher {
    private final KafkaTemplate<String, TransactionEvent> kafkaTemplate;

    public void publishTransaction(TransactionEvent event) {
        kafkaTemplate.send("transactions", event.getId(), event);
    }
}

At Edward Jones, Kafka processed 500K+ daily transactions across multiple consumer groups. The key design decisions:

• Topic partitioning by transaction ID — ensured ordering for related events
• Consumer groups for parallel processing — multiple instances of each consuming service
• Dead letter topics — failed events routed to a DLT for manual review instead of blocking the pipeline
• Idempotent consumers — services handled duplicate events gracefully (critical for financial transactions)

Containerizing 20+ Spring Boot microservices with Docker and deploying them to Kubernetes on AWS transformed the team's deployment velocity. Before containers, environment setup took hours of manual configuration. After Docker, a new developer could spin up the entire system in minutes.

Docker Strategy

Each microservice has a multi-stage Dockerfile that builds the application and creates a minimal runtime image:

# Build stage
FROM maven:3.9-eclipse-temurin-17 AS build
WORKDIR /app
COPY pom.xml .
RUN mvn dependency:go-offline
COPY src ./src
RUN mvn package -DskipTests

# Runtime stage
FROM eclipse-temurin:17-jre-alpine
COPY --from=build /app/target/*.jar app.jar
EXPOSE 8080
ENTRYPOINT ["java", "-jar", "app.jar"]

Key Docker practices that cut setup time by 40%:

• Multi-stage builds — build images stayed large, runtime images stayed small (~150MB vs 800MB+)
• Docker Compose for local development — one docker-compose up started all services, databases, and Kafka
• Consistent environment configuration — Spring profiles (application-docker.yml) ensured local, staging, and production used the same container images with different config
• Layer caching — dependency download layer cached separately from source code, speeding up rebuilds

Kubernetes on AWS

Kubernetes deployment configurations defined resource limits, health checks, scaling policies, and rolling updates for each service:

• Readiness probes — Spring Boot Actuator's /actuator/health/readiness endpoint told Kubernetes when a service was ready to accept traffic
• Liveness probes — /actuator/health/liveness detected hung processes and triggered automatic restarts
• Resource limits — each service had CPU/memory requests and limits tuned to its actual usage
• Horizontal Pod Autoscaler — Kafka consumer services scaled based on consumer lag, not just CPU

The CI/CD pipeline at Edward Jones achieved a 99% deployment success rate with zero downtime rollbacks. That number wasn't luck — it was the result of automated quality gates, incremental rollouts, and a pipeline design that treated each microservice as an independent deployment unit.

Jenkins Pipeline Design

Each microservice had its own Jenkins pipeline with five stages:

In financial services, security isn't a feature — it's a regulatory requirement. Every API endpoint, every inter-service call, and every piece of data in transit needed authentication, authorization, and encryption. Spring Security made this manageable, but the microservices context added complexity that monolithic applications don't face.

JWT Authentication Flow

The authentication flow for React frontend to Spring Boot backend:

@RestController
@RequestMapping("/api/v1/compliance")
public class ComplianceController {

    @PreAuthorize("hasRole('COMPLIANCE_REVIEWER')")
    @GetMapping("/reviews/pending")
    public List<ComplianceReview> getPendingReviews() {
        return complianceService.getPendingReviews();
    }

    @PreAuthorize("hasAnyRole('ADMIN', 'COMPLIANCE_MANAGER')")
    @PostMapping("/reviews/{id}/approve")
    public ComplianceReview approveReview(@PathVariable Long id) {
        return complianceService.approveReview(id);
    }
}

Inter-Service Security

Services calling other services also needed authentication. Two approaches worked in practice:

• Service-to-service JWT — services had their own service accounts with JWT tokens for inter-service calls
• Token propagation — for user-context-sensitive calls, the original user's JWT was forwarded through the call chain

The most impactful project during my time at Edward Jones was the Enterprise Document Automation MVP — a system that transformed compliance document reviews from a manual, hours-long process into an automated pipeline that runs in minutes.

The Problem

Compliance reviews at a financial services firm involve processing thousands of documents: client agreements, regulatory filings, account forms, and audit materials. Before automation:

• Manual data extraction — compliance analysts manually read each document and entered data into review systems
• 1,200+ hours annually — spent on repetitive document handling tasks
• Error-prone — manual data entry introduced inconsistencies that created downstream compliance risks
• Slow turnaround — document validation cycles took hours, creating bottlenecks in client onboarding

The Solution: Microservices + ABBYY FlexiCapture

The document automation system was built as a set of coordinated Spring Boot microservices:

Document Ingestion Service — received documents from multiple channels (email, upload, API), validated file types, and published a DocumentReceived event to Kafka.

OCR Processing Service — consumed DocumentReceived events and integrated with ABBYY FlexiCapture for intelligent document recognition. ABBYY extracted structured data from unstructured documents — fields, tables, signatures — with high accuracy.

Compliance Validation Service — consumed DocumentProcessed events and ran the extracted data against compliance rules. Rules were configurable per document type and regulation.

Notification Service — published results to compliance analysts' dashboards and triggered alerts for documents that failed validation or needed human review.

Integration with Pega BPM — the workflow layer that managed the human review process. When documents needed manual review, Pega BPM routed them to the appropriate compliance analyst based on document type, complexity, and analyst workload.

Architecture Decisions That Mattered

Why microservices instead of a monolith? Each stage of document processing had different scaling requirements. OCR processing was CPU-intensive and needed more instances during peak periods. Compliance validation was I/O-bound and scaled differently. Separating them into microservices allowed independent scaling.

Why Kafka instead of REST? Document processing is inherently asynchronous. A document uploaded at 9 AM doesn't need to be validated by 9:00:01 AM. Kafka provided reliable delivery (no lost documents), parallel processing (multiple OCR workers), and a complete audit trail (every event logged to a topic).

Why Pega BPM integration? Compliance requires human judgment for edge cases. Rather than building a custom workflow engine, integrating with Pega BPM — which Edward Jones already used for business process management — provided configurable routing rules, SLA tracking, and audit trails.

Spring Boot isn't the only option for Java microservices. Quarkus and Micronaut have gained significant traction, especially for cloud-native and serverless deployments. Here's how they compare based on enterprise experience:

Why Spring Boot won at Edward Jones:

• Existing expertise — the team already knew Spring. The cost of retraining for marginal performance gains wasn't justified.
• Ecosystem depth — Spring Security, Spring Data, Spring Cloud, Spring Kafka — every integration the team needed had a mature Spring solution.
• Enterprise support — VMware (now Broadcom) provides commercial support for Spring. In financial services, vendor support contracts matter for compliance.
• Startup time was acceptable — microservices ran as long-lived processes in Kubernetes, not serverless functions. A 3-second startup on deployment was irrelevant for services that run 24/7.

After building, reviewing, and debugging 20+ enterprise microservices, certain mistakes appear repeatedly across teams.

The Distributed Monolith Anti-Pattern

The most expensive mistake is building a distributed monolith — a system with the operational complexity of microservices but the coupling of a monolith. Symptoms include:

• Synchronized deployments — "we need to deploy Service A, B, and C together or nothing works"
• Shared database schemas — services read from each other's tables directly
• Cascading failures — one service going down takes multiple others with it
• No independent scaling — all services scale together because they're coupled

The fix: strict service boundaries, event-driven communication for data sharing, and the discipline to deploy services independently — even if it means maintaining backward-compatible APIs for a transition period.

The "Too Many Services Too Soon" Mistake

The opposite mistake is decomposing too aggressively. A team of five doesn't need 30 microservices. The operational overhead — monitoring, debugging, deployment pipelines — grows linearly with service count. Start with a modular monolith, identify components that genuinely benefit from independent scaling or deployment, and decompose incrementally.