Interview Questions for API Management and Gateway

In a microservices architecture, where applications are broken down into smaller, independent services, an API gateway acts as a central point of entry for all client requests. Think of it as a receptionist for your microservices. Instead of clients needing to know the location and specifics of each individual service (like navigating a sprawling office building), they interact with the gateway, which then routes requests to the appropriate service. This simplifies client interaction, improves security, and allows for easier management of the overall system.

For example, imagine an e-commerce application with separate services for user accounts, product catalogs, and order processing. The API gateway would receive requests for user login, product browsing, and order placement. It would then forward these requests to the relevant microservices, aggregate the responses, and return a unified response to the client. This hides the complexity of the backend architecture from the client, providing a clean and consistent interface.

API gateways employ various patterns to achieve their functionality. Two common ones are:

• Reverse Proxy: This is the most prevalent pattern. The gateway acts as a reverse proxy, sitting in front of the microservices and receiving all incoming requests. It then forwards these requests to the appropriate backend service based on routing rules. This decouples clients from backend services, enabling changes to the backend without impacting the client.
• Message Broker: In this pattern, the API gateway acts as an intermediary, using a message broker (like Kafka or RabbitMQ) to communicate asynchronously with the microservices. This is particularly useful for handling high volumes of requests or situations requiring loose coupling between services. The gateway receives the request, publishes it to the message queue, and microservices subscribe to relevant messages to process them.

Choosing the right pattern depends on the specific needs of your application. For simpler applications with lower request volumes, a reverse proxy might suffice. For complex applications with high concurrency and asynchronous communication requirements, a message broker pattern offers greater scalability and resilience.

Key features of a robust API gateway include:

• Request Routing: Directing incoming requests to the appropriate backend service.
• Load Balancing: Distributing requests across multiple instances of a service to improve performance and availability.
• Authentication and Authorization: Verifying the identity of clients and controlling access to resources.
• Rate Limiting: Preventing abuse and ensuring fair resource usage.
• Transformation and Protocol Translation: Adapting requests and responses between different formats and protocols.
• Monitoring and Logging: Tracking API usage and identifying potential issues.
• Caching: Storing frequently accessed data to improve performance.
• Security: Implementing various security measures like input validation, output encoding, and protection against common attacks.

These features work together to provide a secure, performant, and scalable interface for accessing microservices.

API gateways handle authentication and authorization through various mechanisms. Authentication verifies the identity of the client, while authorization determines what resources the client is allowed to access. Common methods include:

• API Keys: Unique identifiers assigned to clients for identification.
• OAuth 2.0: A widely used authorization framework allowing clients to access protected resources on behalf of a user without sharing their credentials.
• JSON Web Tokens (JWT): Self-contained tokens containing user information and claims, often used for authentication and authorization.
• Basic Authentication: Using a username and password to authenticate the client.

The gateway can validate credentials against an identity provider or a database, and enforce authorization policies based on roles, permissions, or other criteria. For instance, a gateway might allow only authenticated users to access certain endpoints, or grant different levels of access based on user roles.

API rate limiting is a crucial mechanism for controlling the number of requests a client can make within a specific timeframe. This is essential to prevent abuse, denial-of-service attacks, and ensure fair resource allocation among all clients. Without rate limiting, a malicious actor could flood your API with requests, overwhelming your backend services and causing them to become unresponsive.

Rate limiting is usually implemented by defining limits such as ‘100 requests per minute per IP address’ or ‘1000 requests per hour per user’. When a client exceeds the defined limit, the gateway can respond with an error message, temporarily block the client, or apply other mitigation strategies. This protects your system and ensures a stable and responsive service for legitimate users.

An API gateway handles request routing and load balancing using several techniques. Request routing is typically based on the request path, headers, or other attributes. The gateway uses routing rules to determine which backend service should receive the request. For instance, a request to /users might be routed to the user service, while a request to /products is routed to the product service.

Load balancing distributes requests across multiple instances of a service to prevent any single instance from becoming overloaded. Common load balancing algorithms include:

• Round Robin: Distributing requests sequentially across all instances.
• Least Connections: Sending requests to the instance with the fewest active connections.
• Weighted Round Robin: Distributing requests based on the capacity of each instance.

The gateway monitors the health of each backend service and automatically removes unhealthy instances from the load balancing pool, ensuring that only healthy instances receive requests.

API security is paramount, and the gateway plays a central role. Best practices include:

• Input Validation: Sanitizing and validating all incoming requests to prevent injection attacks.
• Output Encoding: Encoding responses to prevent cross-site scripting (XSS) attacks.
• Authentication and Authorization: As discussed earlier, employing robust methods to verify client identity and control access.
• Rate Limiting: Preventing abuse and denial-of-service attacks.
• HTTPS: Ensuring all communication is encrypted.
• Web Application Firewall (WAF): Protecting against common web attacks like SQL injection and cross-site scripting.
• API Key Management: Implementing secure methods for generating, rotating, and revoking API keys.
• Monitoring and Logging: Tracking API usage and identifying potential security breaches.

Implementing these measures within the API gateway creates a strong security perimeter, safeguarding your microservices and data from potential threats. Remember, security is a layered approach; the gateway forms a vital part of this defense in depth.

API gateway deployment strategies depend heavily on factors like scalability needs, infrastructure, and security requirements. Common approaches include:

• On-premises deployment: The gateway resides within your own data center. This offers maximum control but requires managing the infrastructure yourself. Think of it like having your own private post office – you control everything, but you’re also responsible for maintenance and upgrades.
• Cloud deployment: Leveraging cloud providers like AWS, Azure, or Google Cloud. This offers scalability, elasticity, and reduced operational overhead. Imagine this as using a commercial shipping service – they handle the logistics, allowing you to focus on your packages (APIs).
• Hybrid deployment: A mix of on-premises and cloud deployments. Some APIs might be hosted on-premises for security reasons, while others leverage the cloud for scalability. This is like using a combination of private and commercial shipping, optimizing for cost and security.
• Multi-cloud deployment: Distributing the gateway across multiple cloud providers for redundancy and resilience. This is akin to having multiple shipping options to safeguard against disruptions in any single provider.

Choosing the right strategy requires careful consideration of your organization’s specific needs and resources. A small startup might opt for a cloud-based solution, while a large enterprise with stringent security policies might prefer a hybrid or on-premises approach.

Kong, Apigee, and AWS API Gateway are leading API gateway technologies, each with strengths and weaknesses:

• Kong: Open-source, highly extensible, and known for its plugin architecture allowing customization. It’s very flexible and community-driven but might require more hands-on management. Think of it as a highly customizable car – you can modify it extensively, but it requires mechanical expertise.
• Apigee: A full-fledged API management platform with robust features like analytics, security, and developer portals. It’s a comprehensive solution but comes with a higher cost and potentially steeper learning curve. This is like a luxury car – comprehensive features, but more expensive and complex.
• AWS API Gateway: Tightly integrated with the AWS ecosystem, providing seamless integration with other AWS services. It’s easy to use if you’re already heavily invested in AWS, but less flexible if you need to integrate with other cloud providers. Imagine this as a car perfectly tailored to a specific road (AWS ecosystem), highly efficient within that environment.

The best choice depends on your specific requirements. If you need high customization and are comfortable with open-source, Kong might be ideal. If you need a comprehensive, managed solution, Apigee is a strong contender. For seamless AWS integration, AWS API Gateway is the natural choice.

Monitoring and troubleshooting API gateway performance involves a multi-faceted approach:

• Metrics monitoring: Track key metrics like latency, throughput, error rates, and request volume using tools like Prometheus, Grafana, or cloud-provider-specific monitoring services. Visualizing these metrics helps identify bottlenecks and performance degradation.
• Logging and tracing: Implement robust logging to capture request and response details, errors, and other relevant information. Distributed tracing tools can help track requests across multiple services to pinpoint the source of issues. Think of this as leaving a breadcrumb trail to help navigate the journey of a request.
• Alerting: Set up alerts for critical metrics exceeding predefined thresholds. This allows for proactive identification and resolution of issues before they impact users. Timely alerts are crucial for rapid response.
• Performance testing: Conduct load tests and stress tests to assess the gateway’s capacity and identify potential bottlenecks under different load conditions. This is proactive problem-solving.
• Debugging tools: Utilize debugging tools provided by the gateway platform or integrate with debugging tools like your IDE for deeper insights into code and configurations. This allows for detailed investigation of individual requests.

By combining these approaches, you can gain a comprehensive understanding of the gateway’s performance and quickly diagnose and resolve issues.

API versioning is crucial for managing evolving APIs without breaking existing integrations. It allows you to introduce new features and changes without affecting clients relying on older versions. Common approaches include:

• URI versioning: Including the version number in the API endpoint URL (e.g., /v1/users, /v2/users). This is simple but can lead to many URLs if many versions exist.
• Header versioning: Using an HTTP header (e.g., X-API-Version) to specify the version. This is cleaner than URI versioning but requires clients to include the header.
• Request parameter versioning: Including the version number as a query parameter (e.g., /users?version=2). Similar to header versioning, this approach requires clients to send the version parameter.
• Content negotiation: Using Accept headers to specify the desired media type and version. This is less straightforward than others.

Implications of not implementing proper API versioning include application breakage, compatibility issues, and difficulty maintaining different versions of your API. A well-defined versioning strategy ensures smooth transitions and backward compatibility, preserving the investment made by developers using your API.

Effective API documentation and developer portals are vital for API adoption and success. A well-maintained developer portal serves as a central hub for information about the API, making it easier for developers to use and integrate your APIs. Key aspects include:

• Comprehensive documentation: Provide clear, concise, and up-to-date documentation detailing API endpoints, request/response formats, authentication mechanisms, error codes, and usage examples.
• Interactive API explorer: Allow developers to test API calls directly within the portal, reducing friction during the integration process.
• SDKs and code samples: Offer client SDKs in popular programming languages and provide code samples illustrating common use cases.
• Community forum or support channels: Facilitate communication between developers and API providers, fostering a collaborative environment and quick resolution of issues.
• Registration and key management: Implement a system for developer registration, API key management, and rate limiting. This helps control access to your APIs and prevent abuse.

Tools such as Swagger/OpenAPI, Postman, and Read the Docs are commonly used to create and manage API documentation and developer portals. Investing time and resources in a well-designed portal significantly boosts API adoption and reduces support costs.

My experience with API lifecycle management encompasses all stages, from design and development through deployment, monitoring, and retirement. I’ve worked with various teams to establish processes that streamline each phase. For example, in a previous role, we implemented a CI/CD pipeline for API deployment, automated testing using tools like Postman and JMeter, and utilized a version control system (like Git) to manage API specifications and code. We also defined clear roles and responsibilities, established review processes to ensure API quality, and used analytics to track API usage and identify areas for improvement. This holistic approach ensured consistent quality, rapid deployment, and efficient management of our APIs across their entire lifecycle.

Moreover, I’ve leveraged API management platforms to automate many aspects of this lifecycle. This involved configuring workflows, setting up automated deployments, and using built-in analytics dashboards to monitor API health and usage. This automation significantly reduced manual effort and allowed us to focus on strategic initiatives.

Common challenges in API management include:

• Security vulnerabilities: Protecting APIs from unauthorized access, data breaches, and other security threats requires constant vigilance and the implementation of robust security measures, including authentication, authorization, and input validation.
• Scalability and performance: Ensuring APIs can handle increasing traffic volumes and maintain acceptable performance levels requires careful planning and scaling strategies.
• Monitoring and troubleshooting: Identifying and resolving API performance issues and security vulnerabilities in a timely manner requires effective monitoring and troubleshooting mechanisms.
• API documentation and developer experience: Providing clear, concise, and up-to-date documentation and a positive developer experience is crucial for API adoption.
• API versioning and backward compatibility: Managing different API versions and ensuring backward compatibility can be complex, requiring careful planning and execution.
• Governance and compliance: Adhering to internal policies, industry standards, and regulatory requirements (e.g., GDPR, CCPA) adds complexity to API management.

Successfully addressing these challenges often requires a combination of technical expertise, process improvement, and collaboration across different teams within an organization.

Ensuring API scalability and resilience is paramount for any successful API strategy. Think of it like building a highway system: you need enough lanes (capacity) to handle peak traffic (high demand) and alternative routes (redundancy) in case of accidents (failures).

• Horizontal Scaling: This involves adding more servers to your infrastructure to handle increased load. Imagine adding more lanes to your highway. Cloud platforms like AWS, Azure, and GCP make this incredibly easy to manage automatically.
• Load Balancing: Distributes incoming traffic across multiple servers, preventing any single server from becoming overloaded. This is like having multiple on-ramps to your highway, distributing the cars evenly.
• Caching: Stores frequently accessed data closer to the clients, reducing the load on backend servers. Think of this as pre-building sections of your highway to reduce congestion.
• Redundancy and Failover: Implementing backups and failover mechanisms ensures continuous operation even if one component fails. This is like having alternate routes in case of road closures.
• Asynchronous Processing: Handling requests asynchronously (not blocking the main thread) allows your API to respond quickly, even when performing long-running tasks. This is like having dedicated service roads for maintenance and repair, while keeping the main highway open.

In practice, I’ve used Kubernetes and Docker for container orchestration, allowing for seamless scaling and deployment of API services. I’ve also implemented robust monitoring using tools like Prometheus and Grafana to proactively identify and address performance bottlenecks.

API testing and security scanning are crucial for delivering reliable and secure APIs. I’ve extensively used a range of tools throughout my career, tailoring my approach based on the specific needs of the project.

• Testing Tools: I have experience with Postman for functional testing, writing and executing comprehensive test suites to verify API behavior. For performance testing, I’ve used JMeter to simulate realistic loads and identify potential bottlenecks. Contract testing, crucial for microservices, has been performed with Pact and Spring Cloud Contract.
• Security Scanning Tools: OWASP ZAP is a favorite for penetration testing, identifying vulnerabilities like SQL injection and cross-site scripting. I’ve also leveraged static analysis tools like SonarQube to detect security flaws in the API code itself. Dynamic Application Security Testing (DAST) tools are also frequently used during various stages of the development pipeline to catch vulnerabilities in real-time.

For example, in a recent project, we integrated OWASP ZAP into our CI/CD pipeline. This automated security scanning before each deployment, ensuring that known vulnerabilities were addressed before they reached production.

CI/CD pipelines are essential for automating API deployment and ensuring a smooth and consistent release process. Think of it as a well-oiled assembly line for your APIs. My experience spans various tools and approaches.

• Version Control: Git is the foundation, allowing for collaborative development and tracking of changes. I typically use Gitflow for managing branches and releases.
• Build Tools: Maven and Gradle are common choices for building and packaging APIs. They automate the compilation, testing, and packaging of the code.
• Automated Testing: Integration into the pipeline ensures that all tests (unit, integration, and end-to-end) pass before deployment.
• Deployment Tools: Tools like Jenkins, GitLab CI, or Azure DevOps automate the deployment to various environments (development, testing, staging, production). Containerization with Docker and orchestration with Kubernetes are often leveraged for improved scalability and portability.
• Monitoring and Logging: Integration with monitoring and logging tools provides insights into the performance and health of the deployed APIs. Tools like ELK stack (Elasticsearch, Logstash, Kibana) or Grafana are usually included for this purpose.

In one project, we implemented a blue/green deployment strategy using Jenkins. This minimized downtime during deployments by switching traffic between two identical environments, ensuring zero-downtime releases.

Graceful handling of API failures is critical for a positive user experience. Imagine your favorite app crashing – frustrating, right? We want to avoid that.

• Error Handling: Implement robust error handling within the API code to catch exceptions and return informative error messages. Generic 500 errors are unhelpful; specific error codes and messages are essential.
• Circuit Breakers: Prevent cascading failures by stopping requests to failing services temporarily. This prevents a single faulty component from bringing down the entire system.
• Retry Mechanisms: Implement logic to retry failed requests after a short delay. Transient network issues can be overcome with intelligent retries.
• Rate Limiting: Prevent abuse and protect your API from denial-of-service attacks by limiting the number of requests from a single client.
• Detailed Logging: Comprehensive logging provides crucial insights into failures, aiding in debugging and problem resolution.

For example, I’ve used Hystrix (Netflix OSS) to implement circuit breakers, ensuring resilience and preventing cascading failures across multiple microservices.

API contract testing ensures that the provider and consumer of an API agree on the structure and format of the data exchanged. Think of it like a legally binding contract between two parties.

It verifies that the API’s response matches the expected structure as defined in the contract. This ensures compatibility and reduces integration issues between different services. Changes to the API are carefully validated against the contracts, preventing breaking changes that could affect consumers.

Tools like Pact help automate this process by generating and verifying contracts. A contract defines what requests are expected and the corresponding responses. The provider (API) tests to ensure it fulfills the contract, while the consumer (client) verifies the API adheres to it.

API analytics and monitoring provide crucial insights into API usage, performance, and health. Imagine a dashboard showing real-time performance metrics.

• Metrics Collection: Gather data such as request latency, error rates, throughput, and request volume using tools like Prometheus or Datadog.
• Logging: Capture detailed logs of all requests and responses for debugging and troubleshooting.
• Visualization and Dashboards: Use dashboards to visualize key metrics, providing at-a-glance insights into API performance and health (Grafana, Kibana).
• Alerting: Set up alerts to notify you of critical events, such as high error rates or performance degradation.
• API Usage Analysis: Track API usage patterns to identify trends and optimize resource allocation.

In a recent project, I implemented a comprehensive monitoring system using Prometheus and Grafana. This allowed us to identify and resolve a performance bottleneck quickly, preventing a major outage.

Choosing the right API gateway is a crucial decision. It’s the central hub for all your APIs, so the choice impacts performance, security, and scalability.

• Scalability and Performance: The gateway must be able to handle the expected traffic volume and ensure low latency. Cloud-native gateways are often preferred.
• Security: Strong security features, such as authentication, authorization, and rate limiting, are essential. Consider OAuth2 support and Web Application Firewall (WAF) capabilities.
• Management Features: Easy-to-use management tools for configuring, monitoring, and managing APIs are critical for efficient operation.
• Integration with Existing Infrastructure: The gateway must integrate seamlessly with your existing infrastructure, including your CI/CD pipeline.
• Cost: Consider the total cost of ownership, including licensing fees, infrastructure costs, and maintenance.
• Extensibility and Customization: The ability to extend and customize the gateway to meet specific requirements is vital as needs evolve.

Factors such as the size of your organization, the number of APIs you have, and your security requirements will heavily influence the decision. I’ve worked with both open-source gateways like Kong and commercial solutions like Apigee, tailoring the selection based on the specific context of each project.

Managing API keys and access tokens is crucial for securing your APIs. Think of API keys as your apartment keys – they grant broad access. Access tokens are more like temporary guest passes, providing access only for specific actions and for a limited time. Effective management involves several key strategies:

• Secure Storage: Never hardcode API keys directly into your application code. Use environment variables or secure configuration management tools like HashiCorp Vault or AWS Secrets Manager.
• Rotation and Expiration: Regularly rotate API keys to mitigate the impact of compromised keys. Set expiration dates for access tokens to limit their validity.
• Rate Limiting: Implement rate limiting to prevent abuse and denial-of-service attacks. This limits the number of requests an API key or token can make within a given timeframe.
• Centralized Management: Use an API gateway or dedicated key management system to centralize the creation, distribution, revocation, and monitoring of API keys and access tokens. This provides a single pane of glass for managing security.
• Monitoring and Auditing: Track API key usage and access token activity to identify suspicious behavior and potential security breaches. This includes logging all API calls, user IDs, and timestamps.

For example, in a typical e-commerce application, a merchant might have a long-lived API key for their application to interact with the payment gateway. However, individual customer transactions would leverage short-lived access tokens obtained through OAuth 2.0, ensuring that if a token is compromised, the damage is limited.

OAuth 2.0 and OpenID Connect (OIDC) are authorization frameworks that work together seamlessly to manage user authentication and authorization. Think of OAuth 2.0 as the protocol for granting access to protected resources, and OIDC as the protocol for verifying the user’s identity.

OAuth 2.0 focuses on authorization. It allows a client (like a mobile app) to access a protected resource (like user data on a server) on behalf of a user without needing the user’s credentials directly. It uses access tokens, which are short-lived and grant specific permissions.

OpenID Connect (OIDC) builds upon OAuth 2.0 by adding an identity layer. It provides a way for the client to verify the identity of the user, receiving information like the user’s ID and other claims. This helps identify *who* is accessing the resource.

In practice, a typical flow might be: a user logs in using their Google account (using OIDC for authentication), and Google returns an ID token. This token is then exchanged for an access token via OAuth 2.0, which the application uses to make requests to a protected API, fetching the user’s data without ever directly knowing the user’s Google password.

Modern API gateways are designed to handle various protocols. The key is abstraction: the gateway acts as a translator, ensuring consistent interaction regardless of the underlying protocol.

• REST: RESTful APIs are widely used and generally straightforward to handle. The gateway routes requests based on HTTP methods (GET, POST, PUT, DELETE) and endpoints.
• GraphQL: GraphQL APIs offer flexibility, allowing clients to request specific data fields. The gateway can interact with GraphQL servers, often needing to transform the response to a consistent format for client consumption.
• SOAP: SOAP APIs, while older, are still in use. The gateway can handle SOAP requests, using technologies like XML parsing and potentially transforming the response into a more modern JSON format for better client compatibility.

A robust API gateway uses protocol-agnostic policies and transformations. For example, it might apply rate limiting, authentication, and logging regardless of whether the API uses REST, GraphQL, or SOAP. It will then translate from the request protocol into a format the backend service understands and vice versa for responses.

API transformations and data mapping are essential for integrating disparate systems. Imagine needing to connect a legacy system with a modern microservice. Their data formats might be completely different.

Transformations modify the structure or content of API requests and responses. For example, you might need to convert XML to JSON, or rearrange data fields. This often involves using techniques like XSLT, JSONPath, or custom scripting within the API gateway.

Data Mapping defines the correspondence between data fields in different systems. It helps ensure that data flows correctly between the systems. For instance, a customer’s ‘shipping address’ field in one system might map to separate ‘street’, ‘city’, ‘state’, and ‘zip code’ fields in another.

Example: A legacy SOAP API providing customer data in XML needs to be consumed by a modern React application expecting JSON. The API gateway would perform an XML-to-JSON transformation and map the relevant XML fields to the expected JSON structure.

Tools like Apache Camel or MuleSoft Anypoint Platform facilitate these transformations and mappings.

Caching plays a vital role in optimizing API performance. It stores frequently accessed data closer to the client, reducing the need to repeatedly fetch it from the backend. Think of it like a well-stocked pantry—you don’t need to go to the grocery store every time you need something common.

Types of Caching:

• Edge Caching: Caches data at the edge of the network, closer to clients. This reduces latency and bandwidth usage. Content Delivery Networks (CDNs) often provide edge caching services.
• API Gateway Caching: The API gateway itself can cache responses from backend services. This is particularly effective for read-only operations.
• Backend Caching: Backend services can also cache data within their own infrastructure, further improving performance.

Benefits:

• Reduced Latency: Faster response times for clients.
• Reduced Server Load: Fewer requests to backend servers.
• Improved Scalability: Easier to handle increased traffic.

Effective caching requires careful consideration of cache invalidation strategies to ensure data consistency. This often involves mechanisms like time-to-live (TTL) settings, or using cache tags to invalidate entries when underlying data changes.

API gateways must support a variety of authentication mechanisms to cater to different security needs and integrations.

• API Keys: Simple, but less secure for sensitive operations.
• OAuth 2.0: Industry standard for authorization, allowing clients to access resources on behalf of users.
• OpenID Connect (OIDC): Extends OAuth 2.0 by adding identity verification.
• JWT (JSON Web Tokens): Compact and self-contained tokens used for authentication and authorization.
• Basic Authentication: Simple username/password scheme, generally less preferred for API security.
• Mutual TLS (mTLS): Uses client certificates for authentication, providing strong security for machine-to-machine communication.

A well-designed API gateway allows you to configure these authentication methods as needed. It can enforce policies based on the chosen mechanism and validate tokens to ensure authenticity. You can even have multiple authentication schemes available for different API endpoints, tailoring security based on specific needs.

Serverless architectures significantly impact API design, often leading to more granular, event-driven APIs. Instead of managing and scaling large monolithic servers, you deploy individual functions triggered by events.

Impact on API Design:

• Microservices: Serverless naturally lends itself to microservices, where each function performs a specific task. This allows for independent scaling and deployment of individual API components.
• Event-Driven Architecture: APIs often become event-driven, reacting to triggers rather than constantly polling for updates. This increases efficiency and reduces latency.
• Statelessness: Serverless functions are usually stateless, meaning each invocation is independent. This simplifies scaling and deployment, but requires managing state externally (e.g., using a database or message queue).
• API Gateway Importance: The API gateway becomes even more critical in serverless architectures, acting as the central point of entry and managing routing, authentication, and other cross-cutting concerns.

For example, an image processing API might use a serverless function triggered when a new image is uploaded. The function would process the image and then invoke another function to store the processed image in cloud storage. The API gateway would handle the initial upload and orchestrate the various serverless functions involved. The overall design is more modular, scalable, and cost-effective than traditional approaches.