Chapter 12: Building Scalable Applications

In this section

• 12.1 Designing Microservices with Clojure and NoSQL
  • Microservices Architecture: Principles, Benefits, and Challenges for Clojure and NoSQL
    Explore the fundamentals of microservices architecture, its benefits, challenges, and how Clojure and NoSQL technologies fit into this paradigm, offering scalability, resilience, and flexibility for modern applications.
  • Implementing Microservices in Clojure: A Comprehensive Guide
    Explore the intricacies of building microservices with Clojure, leveraging frameworks like Pedestal and Reitit, and integrating with NoSQL databases for scalable solutions.
  • Data Management in Microservices: Optimizing NoSQL for Scalability and Flexibility
    Explore the intricacies of data management in microservices architecture, focusing on NoSQL databases, data consistency, and synchronization strategies.
• 12.2 Event-Driven Architectures and Messaging Systems
  • Event-Driven Architecture (EDA): Core Concepts, Advantages, and Use Cases
    Explore the fundamentals of Event-Driven Architecture (EDA), its advantages, and practical use cases in building scalable, responsive, and decoupled systems using Clojure and NoSQL.
  • Implementing Event-Driven Architecture (EDA) with Clojure
    Explore how to implement Event-Driven Architecture using Clojure, leveraging messaging systems like Apache Kafka and RabbitMQ, and Clojure libraries such as Jackdaw and Langolier.
  • Designing Event Streams with NoSQL: A Comprehensive Guide for Java Developers
    Explore the intricacies of designing event streams using NoSQL databases, focusing on append-only logs, schema evolution, replayability, and eventual consistency management.
• 12.3 Real-Time Data Processing with Stream APIs
  • Stream Processing for Scalable Data Solutions: A Clojure Perspective
    Explore the fundamentals of stream processing, its advantages over batch processing, key concepts like low latency and windowing, and popular frameworks such as Apache Kafka Streams and Apache Flink, all from a Clojure and NoSQL perspective.
  • Implementing Streams in Clojure: A Comprehensive Guide with Onyx
    Explore the implementation of data streams in Clojure using the Onyx platform, focusing on setup, functional transformations, and error handling.
  • Integrating NoSQL Databases with Clojure for Scalable Data Solutions
    Explore techniques for integrating NoSQL databases with Clojure, focusing on streaming data, real-time analytics, and change data capture for scalable and efficient data solutions.
• 12.4 Implementing CQRS and Event Sourcing
  • Understanding CQRS: Command Query Responsibility Segregation for Scalable Data Solutions
    Explore the principles of CQRS, its advantages, design considerations, and practical implementation in Clojure for scalable NoSQL data solutions.
  • Implementing Event Sourcing with Clojure and NoSQL
    Explore the implementation of event sourcing using Clojure and NoSQL databases, focusing on event stores, aggregates, state reconstruction, and snapshots.
  • CQRS and Event Sourcing with Clojure: A Comprehensive Guide
    Explore how to implement CQRS and Event Sourcing in Clojure for scalable, maintainable applications. Learn about defining commands and events, event handling, and maintaining read model consistency.
• 12.5 Case Study: Building a High-Throughput Messaging Platform
  • Requirements Analysis for Scalable Messaging Platforms with Clojure and NoSQL
    Explore the comprehensive requirements analysis for building scalable messaging platforms using Clojure and NoSQL, focusing on functional and non-functional aspects to ensure reliability, scalability, and security.
  • Designing Scalable Architecture with Clojure and NoSQL
    Explore the architecture design principles for scalable applications using Clojure and NoSQL databases, focusing on service-oriented components and technology choices.
  • Clojure and NoSQL: Implementation Details for Scalable Data Solutions
    Explore the implementation details of designing scalable data solutions with Clojure and NoSQL, focusing on concurrency handling, scalability patterns, performance optimization, and monitoring.