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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 Cloud-Native Architectures Introduction 01. Defining Cloud-Native 02. Benefits of Cloud-Native 03. Best Practices for Cloud- Native Development 04. Key Technologies and Tools 05. Use Cases and Success Stories of cloud native What is Native Cloud Architecture? In today's rapidly evolving digital landscape, businesses and organizations face the dual Cloud-native is an innovative approach challenge of staying to designing, building, and running competitive while software applications that takes full delivering applications and services that are both advantage of cloud computing innovative and reliable. To principles and services. At its core, being meet this challenge, a paradigm shift in software cloud-native means reimagining the development and entire software development and deployment has emerged, deployment process to be optimized for and it's known as "Cloud- Native Architectures." the dynamic, scalable, and distributed nature of cloud environments.  Key elements that define a cloud-native approach 1. Microservices Architecture: 2. Containerization: 3. Orchestration: 4. DevOps Culture: Cloud-native Tools like Cloud-native Cloud-native embraces a applications are Kubernetes are applications are DevOps culture, often packaged used to manage typically where decomposed into into containers containers at development and smaller, loosely (e.g., Docker scale. operations teams coupled services containers). Orchestration collaborate closely known as Containers platforms throughout the microservices. encapsulate an automate tasks software These services are application and its such as container development independently dependencies, deployment, lifecycle. Automation, deployable and ensuring scaling, and load continuous can be scaled consistency balancing, integration, and individually, across different simplifying the continuous allowing for environments and management of deployment flexibility and enabling complex cloud- (CI/CD) pipelines agility in seamless native are key development and components of deployment and applications. operations. this culture. scaling.  Key elements that define a cloud-native approach 5. Elasticity: Cloud- native 6. Resilience and 7. API-Centric 8. Immutable Infrastructure: Redundancy: Design: Cloud- applications are Infrastructure Cloud-native native components, designed to be applications applications often including servers elastic, meaning they can prioritize expose well- and networking, automatically resilience and defined APIs, are treated as adapt to changes fault tolerance. allowing services code and can be They are to communicate rapidly replaced in demand. architected to and interact with or recreated. This Resources can be approach ensures provisioned or de- handle failures each other consistency and provisioned gracefully by seamlessly. This reduces the risk of dynamically to using redundancy API-centric configuration handle varying and automated approach drift. workloads recovery facilitates efficiently. mechanisms. flexibility and interoperability.  Key elements that define a cloud-native approach 9. Monitoring and Observability: 10. Infrastructure as Code (IaC): Cloud-native Infrastructure applications are provisioning and instrumented for real-time management are monitoring and automated using observability. Infrastructure as Tools like Code tools like Prometheus, Terraform or AWS Grafana, and CloudFormation. logging solutions This ensures that are used to gain infrastructure is insights into reproducible and application version- behavior. controlled. Benefits of Native Cloud Architecture The adoption of cloud-native principles and practices offers a wide range of benefits for organizations across various industries. These benefits can transform how businesses develop, deploy, and maintain their software applications  key advantages of embracing a cloud-native approach Scalability Resilience Faster Time-to-Market Cost Efficiency Improved Resource Utilization Elasticity Enhanced Development Efficiency High Availability Security Global Reach Faster Innovation Resource Optimization Vendor Agnosticism  Key Technologies and Tools Containerization Container Orchestration Serverless Computing Continuous Integration and Continuous Deployment (CI/CD) Service Mesh Monitoring and Observability API Gateways Infrastructure as Code (IaC) Immutable Infrastructure API Management Database Services Identity and Access Management (IAM) These technologies and tools are foundational to the cloud-native ecosystem and help organizations build scalable, resilient, and agile applications in cloud environments. The choice of specific tools may depend on the cloud provider and the requirements of the application being developed.  Case Study Cloud-native architectures have gained significant popularity due to their ability to deliver scalability, resilience, and agility in the cloud. Here are some real-world organizations that have embraced cloud-native approaches Netflix Spotify Uber Airbnb Lyft Etsy Capital One NASA Jet Propulsion Laboratory (JPL) CERN T-Mobile Conclusion In conclusion, native cloud architecture is a powerful approach to building applications that takes full advantage of the cloud environment. By leveraging cloud-native technologies and services, organizations can create applications that are highly scalable, resilient, and cost-effective. We hope this presentation has provided you with a better understanding of the key principles and benefits of native cloud architecture. Thank you for joining us today.  Thanks! Do you have any questions? Benefits of Cloud-Native The adoption of cloud-native principles and practices offers a wide range of benefits for organizations across various industries. These benefits can transform how businesses develop, deploy, and maintain their software applications. Here are some of the key advantages of embracing a cloud-native approach: ​ Scalability: Cloud-native applications are designed for horizontal scalability. They can easily scale out to handle increased workloads and scale in when demand decreases. This elasticity ensures optimal resource utilization and cost savings. ​ Resilience: Cloud-native architectures promote fault tolerance and resilience. By distributing applications into microservices and leveraging redundancy, they are less susceptible to single points of failure. Automated recovery mechanisms help maintain application availability. ​ Faster Time-to-Market: Cloud-native development practices, such as DevOps and continuous integration/continuous deployment (CI/CD), enable rapid development and release of new features. This agility allows businesses to respond quickly to changing market conditions. ​ Cost Efficiency: Cloud-native applications often benefit from better resource utilization, thanks to auto-scaling and fine-grained resource allocation. This can result in cost savings compared to traditional, static infrastructure setups. ​ Improved Resource Utilization: Containerization and orchestration technologies ensure that resources are used efficiently. Containers are lightweight and can be packed densely on host machines, reducing resource wastage. ​ Elasticity: Cloud-native applications can automatically adapt to fluctuating workloads. Resources are allocated or deallocated dynamically based on demand, ensuring optimal performance and cost-effectiveness. ​ Enhanced Development Efficiency: Cloud-native development encourages a more streamlined and collaborative approach between development and operations teams. This leads to shorter development cycles, reduced manual interventions, and faster bug fixes. ​ High Availability: Cloud-native applications can achieve high availability through load balancing, redundancy, and self-healing mechanisms. Services can be distributed across multiple geographic regions for disaster recovery. ​ Security: Cloud-native architectures often include built-in security features, such as encryption, identity and access management (IAM), and security policies. Additionally, containerization can isolate applications for improved security.  ​ Global Reach: Cloud-native applications can be easily deployed and scaled across multiple regions and data centers, providing a global reach for your services and ensuring low-latency access for users worldwide. ​ Faster Innovation: With cloud-native technologies and practices, organizations can experiment and innovate more rapidly. New features and improvements can be rolled out continuously, allowing businesses to stay competitive in rapidly changing markets. ​ Resource Optimization: Cloud-native architectures emphasize resource optimization, which can lead to reduced infrastructure costs. Organizations pay only for the resources they use, making it more cost-effective than traditional data center setups. ​ Vendor Agnosticism: Cloud-native technologies and containerization allow applications to be more portable. This reduces vendor lock-in, enabling organizations to switch cloud providers or run applications in a hybrid or multi-cloud environment. In conclusion, embracing cloud-native principles and technologies can empower organizations to build more agile, efficient, and resilient applications. These advantages can lead to improved business outcomes and a competitive edge in the fast-paced world of modern technology. Key Technologies and Tools Cloud-native development and deployment rely on a rich ecosystem of technologies and tools designed to facilitate the creation, management, and scaling of cloud-native applications. Here are key technologies and tools commonly used in the cloud-native landscape: ​ Containerization: Docker: Docker is a popular containerization platform that allows you to package applications and their dependencies into containers for consistent deployment across environments. ​ Container Orchestration: Kubernetes: Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It has become the de facto standard for container orchestration. ​ Serverless Computing: AWS Lambda: AWS Lambda is a serverless computing service that lets you run code in response to events without managing servers. Azure Functions: Azure Functions is Microsoft's serverless compute service, providing event-driven, serverless execution of code. Google Cloud Functions: Google Cloud Functions allows you to build serverless functions that automatically scale with traffic. ​ Continuous Integration and Continuous Deployment (CI/CD): Jenkins: Jenkins is an open-source automation server that facilitates CI/CD pipelines for building, testing, and deploying applications.  GitLab CI/CD: GitLab provides built-in CI/CD capabilities, making it easier to manage the software development lifecycle. Travis CI: Travis CI is a cloud-based CI/CD service that automates the testing and deployment of code changes. ​ Service Mesh: Istio: Istio is an open-source service mesh that provides traffic management, security, and observability for microservices deployments. Linkerd: Linkerd is another service mesh solution designed for managing communication between services. ​ Monitoring and Observability: Prometheus: Prometheus is an open-source monitoring and alerting toolkit that collects and stores metrics from various sources. Grafana: Grafana is a popular open-source platform for visualizing and analyzing metrics, logs, and other data. ELK Stack (Elasticsearch, Logstash, Kibana): ELK is a stack used for centralized logging and log analysis. ​ API Gateways: NGINX: NGINX is a popular open-source web server and reverse proxy that can also function as an API gateway. Kong: Kong is an open-source API gateway and microservices management layer. ​ Infrastructure as Code (IaC): Terraform: Terraform is an open-source IaC tool that enables you to define and provision infrastructure using code. AWS CloudFormation: AWS CloudFormation provides templates for defining AWS infrastructure as code. ​ Immutable Infrastructure: Packer: Packer is a tool for creating machine images (e.g., Amazon Machine Images) that are version-controlled and reproducible. HashiCorp Vault: Vault is used for secrets management and encryption in cloud-native applications. ​ API Management: Apigee: Apigee is a full-featured API management platform that enables organizations to design, secure, and scale APIs. AWS API Gateway: AWS API Gateway is a managed service for creating, publishing, and securing APIs. ​ Database Services: Amazon RDS: Amazon RDS provides managed relational database services. Amazon DynamoDB: DynamoDB is a managed NoSQL database service. Google Cloud Spanner: Google Cloud Spanner is a globally distributed, horizontally scalable database service. ​ Identity and Access Management (IAM): AWS IAM: AWS Identity and Access Management provides fine-grained control over access to AWS resources.  Azure Active Directory: Azure AD offers identity and access management services for Azure and Microsoft 365. These technologies and tools are foundational to the cloud-native ecosystem and help organizations build scalable, resilient, and agile applications in cloud environments. The choice of specific tools may depend on the cloud provider and the requirements of the application being developed. Use Cases and Success Stories of cloud native Cloud-native architectures have gained significant popularity due to their ability to deliver scalability, resilience, and agility in the cloud. Here are some real-world use cases and success stories of organizations that have embraced cloud-native approaches: ​ Netflix: Perhaps one of the most famous cloud-native success stories, Netflix moved from a monolithic architecture to a cloud-native microservices architecture. This transition allowed them to scale their streaming platform globally, deliver personalized recommendations, and ensure high availability during peak usage hours. ​ Spotify: Spotify adopted a cloud-native approach to handle its rapidly growing user base and streaming demands. They use microservices, containerization, and Kubernetes to manage their music streaming services efficiently. This has enabled them to continuously deliver new features and updates to their users. ​ Uber: Uber relies on cloud-native technologies to power its ride-hailing platform. They use microservices to handle various aspects of their business, from matching riders with drivers to processing payments. The scalability and resilience provided by cloud-native architecture are crucial for Uber's global operations. ​ Airbnb: Airbnb moved to a cloud-native architecture to handle the high variability in traffic and booking demands. By using containers and Kubernetes, Airbnb can scale its services dynamically during peak seasons and optimize costs during quieter periods. ​ Lyft: Similar to Uber, Lyft uses cloud-native principles to operate its ride-sharing platform. They rely on Kubernetes for container orchestration and manage microservices to handle various aspects of their service. This cloud-native approach ensures a seamless experience for riders and drivers, even during high-demand times. ​ Etsy:  Etsy, an e-commerce platform, transitioned to cloud-native architecture to improve the scalability and reliability of its online marketplace. They use microservices, containerization, and automated deployment pipelines to deliver a smooth shopping experience for customers. ​ Capital One: Capital One, a financial services company, embraced cloud-native architecture to transform its digital banking services. By adopting microservices and containerization, Capital One improved its ability to develop and deploy new banking features quickly while maintaining security and compliance. ​ NASA Jet Propulsion Laboratory (JPL): JPL leveraged cloud-native principles to process and analyze data from Mars rovers and other space missions. They use Kubernetes for managing containers and have created an open-source project called "OpenMCT" for real-time data visualization and analysis. ​ CERN: The European Organization for Nuclear Research (CERN) uses cloud-native technologies to manage and process massive amounts of data generated by the Large Hadron Collider (LHC). Kubernetes and containerization have been instrumental in their data analysis pipelines. ​ T-Mobile: T-Mobile moved to a cloud-native architecture to modernize its IT infrastructure and deliver new services to customers. They use Kubernetes for container orchestration and implement CI/CD pipelines to accelerate software delivery. These use cases demonstrate how cloud-native architectures have enabled organizations to achieve scalability, resilience, and agility in various domains, from entertainment and transportation to finance and space exploration. Cloud-native technologies have become essential for staying competitive and responsive to rapidly changing customer demands.