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Backend Development in Application Development

$249.00
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Course access is prepared after purchase and delivered via email
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Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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This curriculum spans the breadth of backend development work seen in multi-workshop technical programs, covering architecture, data, security, and operations with the same depth as internal capability initiatives in mid-to-large engineering organisations.

Module 1: System Architecture and Design Principles

  • Selecting between monolithic, microservices, and modular monolith architectures based on team size, deployment frequency, and domain complexity.
  • Defining bounded contexts in domain-driven design to align service boundaries with business capabilities and reduce coupling.
  • Implementing API gateways to manage routing, authentication, and rate limiting across distributed services.
  • Evaluating trade-offs between REST, GraphQL, and gRPC for inter-service communication based on payload size, client needs, and latency requirements.
  • Designing asynchronous communication patterns using message brokers like Kafka or RabbitMQ to decouple producers and consumers.
  • Establishing versioning strategies for public APIs to support backward compatibility during schema evolution.

Module 2: Data Modeling and Persistence Strategies

  • Choosing between relational and NoSQL databases based on data consistency requirements, query patterns, and scalability needs.
  • Designing database schemas with normalization versus denormalization trade-offs for read-heavy versus write-heavy workloads.
  • Implementing connection pooling to manage database resource utilization under high concurrency.
  • Configuring indexing strategies to optimize query performance while minimizing write overhead and storage costs.
  • Planning for data migration workflows using tools like Flyway or Liquibase with zero-downtime deployment constraints.
  • Enforcing data integrity through constraints, transactions, and application-level validation across distributed systems.

Module 3: Authentication, Authorization, and Security

  • Integrating OAuth 2.0 and OpenID Connect for third-party identity providers while managing token lifetime and refresh mechanisms.
  • Implementing role-based (RBAC) and attribute-based (ABAC) access control to enforce fine-grained permissions.
  • Securing APIs with signed JWTs and validating scopes on each request in distributed environments.
  • Applying secure session management using HTTP-only cookies or token storage with CSRF protection.
  • Conducting regular security audits of dependency trees using SCA tools to detect known vulnerabilities in libraries.
  • Encrypting sensitive data at rest and in transit using TLS 1.3 and database-level encryption with key rotation policies.

Module 4: Backend Performance and Scalability

  • Designing caching layers using Redis or Memcached with appropriate eviction policies and cache invalidation strategies.
  • Optimizing database queries by analyzing execution plans and eliminating N+1 query patterns in ORM usage.
  • Scaling stateless services horizontally using container orchestration platforms like Kubernetes with auto-scaling rules.
  • Implementing rate limiting and circuit breakers to prevent cascading failures during traffic spikes.
  • Profiling application performance using APM tools to identify bottlenecks in CPU, memory, or I/O usage.
  • Sharding large datasets across multiple database instances based on tenant or geographic distribution.

Module 5: Event-Driven Systems and Messaging

  • Designing idempotent message consumers to handle duplicate deliveries in distributed messaging systems.
  • Choosing between at-least-once and exactly-once delivery semantics based on business criticality and infrastructure support.
  • Implementing event sourcing patterns with event stores to reconstruct state and audit data changes.
  • Managing schema evolution in event payloads using schema registries like Confluent Schema Registry.
  • Monitoring message queue backlogs to detect consumer lag and trigger operational alerts.
  • Ensuring message ordering guarantees when required by partitioning topics by key in distributed brokers.

Module 6: Deployment, CI/CD, and Infrastructure as Code

  • Configuring CI/CD pipelines with automated testing, security scanning, and deployment gates for staging environments.
  • Managing environment-specific configurations using externalized config servers or secure secret stores.
  • Implementing blue-green or canary deployments to reduce risk during production releases.
  • Defining infrastructure using Terraform or Pulumi with version-controlled modules for reproducibility.
  • Automating rollback procedures triggered by health check failures or anomaly detection in metrics.
  • Enforcing immutability of deployed artifacts by building once and promoting across environments.

Module 7: Observability and Operational Resilience

  • Instrumenting applications with structured logging to enable efficient querying and correlation across services.
  • Configuring distributed tracing with OpenTelemetry to track request flows across service boundaries.
  • Setting up alerting rules based on SLOs and error budgets rather than raw thresholds to reduce noise.
  • Designing health checks that reflect actual service dependencies and readiness for traffic.
  • Conducting chaos engineering experiments to test failure recovery in staging environments.
  • Archiving and rotating logs and metrics based on retention policies and compliance requirements.

Module 8: API Design and Developer Experience

  • Documenting APIs using OpenAPI Specification with examples, error codes, and rate limit details.
  • Providing SDKs or client libraries for major programming languages to reduce integration effort.
  • Enforcing consistent error response formats across all endpoints to simplify client handling.
  • Implementing pagination, filtering, and sorting standards to support large dataset queries.
  • Monitoring API usage patterns to identify underutilized endpoints or performance outliers.
  • Establishing deprecation policies with advance notice and migration paths for retiring endpoints.