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Advanced System Design Principles This presentation explores key advanced system design principles and their importance in building robust and efficient software and hardware systems. Introduction System design is a critical Technology advancements Advanced system...

Advanced System Design Principles This presentation explores key advanced system design principles and their importance in building robust and efficient software and hardware systems. Introduction System design is a critical Technology advancements Advanced system design phase in the software and have made systems more principles help in achieving the hardware development complex, requiring careful desired objectives. process. consideration of various factors.  Scalability Scalability is crucial for Employ load balancing to handling increasing load 02 evenly distribute incoming or growth in users or requests. data volume. 01 03 Distribute workloads Understand the difference across multiple servers or between horizontal and nodes using distributed vertical scaling. architecture. 04 Availability and Fault Tolerance Availability ensures the system remains operational and accessible despite failures or maintenance. Duplicate critical components for redundancy. Implement failover mechanisms to switch to backup systems. Continuously monitor the system's health and set up alerts.  Maintainability Use version control to track Maintainability is the ease of changes. modifying, updating, or repairing a system. 01 04 02 Create clear and Break the system into comprehensive smaller, cohesive modules or 03 documentation. microservices.  Performance Optimization involves maximizing the Key considerations include: efficiency of a system to Profiling and benchmarking: minimize response times and 01 Identify performance bottlenecks. resource usage 04 02 Key considerations include: Key considerations include: Caching: Store frequently accessed data in Asynchronous processing: Offload 03 memory to reduce database or network non-critical tasks to background overhead. jobs to improve responsiveness.  Security - is paramount in any system design. Encrypt sensitive data in Regularly apply security Employ authentication and transit and at rest. patches and updates. authorization mechanisms. Data Management - Effective data management is crucial for many systems: Data modeling: Design databases Data consistency: Implement Use NoSQL databases for that suit the application's ACID (Atomicity, Consistency, unstructured or rapidly requirements. Isolation, Durability) transactions changing data. for data integrity. API Design - Well-designed APIs are essential for system integration: Follow RESTful or GraphQL Provide comprehensive Ensure backward compatibility principles for web APIs. documentation and versioning for when making changes APIs.  Monitoring and Analytics - Continuous monitoring and analytics help in understanding system behavior and identifying areas for improvement: Log aggregation: Collect and Metrics collection: Measure key Use tools like ELK stack analyze logs to diagnose issues. performance indicators and set (Elasticsearch, Logstash, Kibana) alerts. or Prometheus for monitoring.  Tools Recommendation *UML Tools*: Tools like Enterprise Architect or Lucidchart can help in creating detailed system architecture diagrams. 01 02 *Load Testing Tools*: Apache JMeter or Gatling for simulating heavy loads on systems to test scalability. *Distributed Systems Design Tools*: Apache ZooKeeper for 03 distributed coordination or Apache Kafka for distributed messaging. Conclusion Advanced system design principles are crucial for creating systems that can handle the complexities of modern technology. These principles, including scalability, availability, maintainability, performance optimization, security, data management, API design, and monitoring, provide a foundation for building robust, efficient, and reliable systems. Keep in mind that system design is an iterative process, and it's essential to continuously assess and improve your designs as technology evolves. Thank you for your time and attention 🙂 Integration patterns Integration patterns are a set of design solutions or best practices used in software architecture to address the challenges of integrating different software systems, components, and services within an organization. These patterns provide standardized approaches to solve common integration problems and help ensure that disparate systems can work together effectively and reliably. Integration patterns are especially valuable in enterprise-level applications where various systems and data sources need to communicate and share information. key aspects of integration patterns Common Integration Challenges: Integration patterns address issues such as data consistency, communication protocols, message formats, error handling, and more. These challenges arise when you need to connect systems that may have different technologies, data formats, or communication requirements. Patterns Catalog: There is a well-known catalog of integration patterns called "Enterprise Integration Patterns" (EIP), introduced in the book of the same name by Gregor Hohpe and Bobby Woolf. EIP provides a comprehensive set of patterns for various integration scenarios. key aspects of integration patterns Messaging Patterns: A significant portion of integration patterns focuses on messaging, including patterns for message routing, transformation, aggregation, and message queuing. These patterns are particularly important for asynchronous communication and event-driven architectures. Service Integration Patterns: With the rise of service-oriented architecture (SOA) and microservices, there are patterns that address the integration of services, such as service discovery, load balancing, and API gateway patterns. Data Integration Patterns: Integration patterns also cover data integration concerns, including patterns for data synchronization, data transformation, and data replication between different data stores. key aspects of integration patterns Event-Driven Patterns: As event-driven architecture becomes more prevalent, integration patterns for handling events and building event-driven systems have gained significance. These patterns include event sourcing, publish-subscribe, and event-driven messaging patterns. Security and Error Handling: Integration patterns often include solutions for handling security concerns in integration, as well as error handling and compensation strategies to ensure system reliability key aspects of integration patterns Technology-Agnostic: Integration patterns are typically technology-agnostic, which means they can be applied using a variety of programming languages, middleware, and tools. They provide a conceptual framework that can be adapted to different technology stacks. Best Practices: Integration patterns often encapsulate best practices and lessons learned from real-world integration projects, helping developers and architects make informed design decisions. Integration pattern styles Enterprise Service Bus (ESB) Style GraphQL Style Service-Oriented Architecture (SOA) Style Event-Driven Architecture (EDA) Style Microservices Style Data Integration Style Message-Oriented Middleware (MOM) Style Cloud-Native Integration Style RESTful API Style Hybrid Integration Style Enterprise Service In this style, an ESB acts as a centralized messaging and integration hub. It facilitates Bus (ESB) Style communication and data exchange between various services and systems. ESBs provide features like message routing, transformation, and protocol mediation, making it easier to integrate diverse applications. Service-Oriented Architecture (SOA) & Microservices SOA promotes the use of loosely coupled, Microservices architecture decomposes reusable services that communicate large applications into small, independent services that communicate through through standardized interfaces (e.g., web lightweight protocols like HTTP or message services). queues. Integration patterns in the SOA style focus Integration patterns in the microservices on defining and orchestrating services to style deal with service discovery, load fulfill specific business processes. balancing, API gateways, and event-driven communication between microservices. Message-Oriented Middleware (MOM) Style & RESTful API Style In this style, message-oriented middleware Representational State Transfer (REST) is a technologies like RabbitMQ, Apache Kafka, style for designing networked applications, or JMS (Java Message Service) are used to often using HTTP as the communication facilitate asynchronous communication. protocol. Integration patterns in the MOM style Integration patterns in the RESTful API include publish-subscribe, message style focus on designing RESTful endpoints, queuing, and message transformation. resource representations, and handling HTTP methods (GET, POST, PUT, DELETE). GraphQL Style GraphQL is a query language for APIs that allows clients to request exactly the data they need. Integration patterns for GraphQL involve defining GraphQL schemas, resolvers, and optimizing queries for efficient data retrieval. Event-Driven Architecture (EDA) Style & Data Integration Style EDA promotes the use of events and event Data integration patterns focus on sharing brokers to enable loosely coupled, real-time and synchronizing data between different communication between components. data sources and systems. Integration patterns in the EDA style Patterns include data replication, data include event sourcing, event-driven transformation, data virtualization, and data messaging, and event-driven microservices. warehousing. Cloud-Native Integration Style This style emphasizes the use of cloud-native technologies and services for integration, such as serverless computing, container orchestration (e.g., Kubernetes), and cloud-based APIs. Integration patterns in the cloud-native style involve designing for scalability, resilience, and flexibility in cloud environments. Hybrid Integration Style In a hybrid integration approach, organizations combine on-premises systems with cloud-based solutions, using integration patterns to bridge the gap. Patterns include hybrid cloud connectivity, data synchronization, and hybrid API management. These integration pattern styles provide guidance and best practices for architects and developers to choose the most suitable approach for their specific integration needs. The choice of style often depends on factors like the organization's existing technology stack, business requirements, scalability needs, and the desired level of decoupling between integrated components. Thank you for listening  IT 204 – Week 5 z Understanding Microservices Architecture z Microservices ▪ Microservices is an architectural approach to software development where a complex application is decomposed into a collection of small, independent, and loosely coupled services. ▪ Each microservice focuses on a specific business capability or function and operates as a standalone unit, communicating with other microservices through well-defined APIs. ▪ Microservices allows for greater agility, scalability, and maintainability in software systems, as each microservice can be developed, deployed, and updated independently, making it easier to adapt to changing requirements and technological advancements. z Rise of Microservices The rise of microservices has been driven by factors such as cloud computing, containerization, DevOps practices, and the need for rapid software delivery in the digital age. Why Microservices Matter Scalability, Agility, Fault Isolation, Technology Diversity, Team Autonomy, Continuous Delivery, Improved Maintenance. z Evolution of Software Architectures Monolithic Architecture - an entire software application is designed as a single, tightly integrated unit. Monolithic applications are typically easier to develop initially but can become complex and unwieldy as they grow. Microservices Architecture - these microservices can be developed, deployed, and scaled independently, and they communicate with each other through well-defined APIs. Microservices promote agility, scalability, and maintainability, making it easier to adapt to changing requirements and technologies. z Characteristics of Microservices ▪ Decentralization ▪ Componentization ▪ Independence ▪ Scalability ▪ Resilience ▪ Technology Agnosticism z Benefits of Microservices ▪ Scalability ▪ Rapid Development and Deployment ▪ Improved Fault Isolation ▪ Enhanced Resilience ▪ Efficient Technology Adoption z Challenges of Microservices ▪ Complexity ▪ Distributed Systems Challenges ▪ Data Management ▪ Testing and Monitoring ▪ Consistency z Microservices vs. Monolithic: A Detailed Comparison ▪ Scalability ▪ Development Speed ▪ Maintenance ▪ Deployment and Rollback ▪ Flexibility and Technology Choices z Key Technologies and Tools ▪ Containers (Docker) ▪ Container Orchestration (Kubernetes) ▪ API Gateways ▪ Service Discovery (Consul, etcd) ▪ Logging and Monitoring (ELK Stack, Prometheus) ▪ Message Brokers (Kafka, RabbitMQ) z Design Principles ▪ Single Responsibility Principle ▪ Separation of Concerns ▪ Microservices Communication Patterns ▪ Microservices Versioning ▪ Data Management Strategies z Microservices Communication Patterns ▪ Synchronous vs. Asynchronous Communication ▪ RESTful APIs ▪ Message Queues (RabbitMQ, Kafka) ▪ gRPC and Protocol Buffers z Deployment and Scalability ▪ Blue-Green Deployment ▪ Canary Deployment ▪ Autoscaling ▪ Microservices Observability z Security in Microservices ▪ Authentication and Authorization ▪ Data Encryption ▪ API Security ▪ Securing Microservices Communication ▪ Security Best Practices z Best Practices ▪ Documentation ▪ Continuous Integration and Continuous Deployment (CI/CD) ▪ Monitoring and Logging ▪ Testing Strategies ▪ Microservices Version Control ▪ Agile Development Practices z Common Pitfalls ▪ Over-fragmentation ▪ Neglecting Testing and Monitoring ▪ Tight Coupling Between Services ▪ Inadequate Documentation ▪ Lack of a Clear Migration Plan  z Flip Classroom Requirements: Topic: Future Trends ▪ Graded per Group 1. Serverless Computing and Microservices ▪ Report per given topic 2. Edge Computing and Microservices ▪ Provide an Architectural Illustration for each topic 3. AI and Machine Learning in Microservices ▪ How it does apply to the Modern Software Development 4. Blockchain and Microservices Note: You can use a structure as a starting point and add more details, examples, and visuals to create an informative and engaging presentation. z Case Studies Companies: Case Questions: 1. Netflix 1. How They Uses Microservices? 2. Uber 2. Microservices in Architecture 3. GCash 3. Impact on Business and Scalability 4. Spotify 5. Airbnb 4. Challenges and Solutions 6. Amazon 5. Lesson Learned from the said companies