Interview Questions for API Development and Integration

RESTful APIs (Representational State Transfer) are designed around a set of architectural constraints that promote interoperability, scalability, and simplicity. Think of it like a well-organized library: each book (resource) has a unique address (URI), and you can perform specific actions (methods) on it. Key principles include:

• Client-Server Architecture: The client and server are independent. The server only manages resources; the client initiates requests.
• Statelessness: Each request contains all the information needed to understand it; the server doesn’t store context between requests. This allows for horizontal scaling easily.
• Cacheability: Responses can be cached to improve performance. Think of it like a browser caching a webpage.
• Uniform Interface: A consistent way to interact with resources through standard HTTP methods.
• Layered System: The client might not be directly aware of which server it’s interacting with, allowing for abstraction and easier maintenance.
• Code on Demand (Optional): The server can extend client functionality by transferring executable code.

Design Patterns: These are reusable solutions to common API design problems. Some examples include:

• Resource-Based URLs: Using URLs that clearly represent the resources (e.g., /users/123).
• Hypermedia Controls (HATEOAS): Including links in responses to guide clients to other related resources, allowing for self-discovery.
• Versioning: Managing API changes over time (e.g., using URL paths like /v1/users or header parameters).

Imagine building a social media API. A RESTful design would involve resources like /users, /posts, and /comments, with HTTP methods like GET (retrieve), POST (create), PUT (update), and DELETE to manage them.

REST and SOAP are both architectural styles for building APIs, but they differ significantly in their approach. REST is lightweight and flexible, using standard HTTP methods and simple data formats like JSON or XML. SOAP, on the other hand, is more heavyweight and complex, relying on XML for both data and messaging, often using WS-* standards for features like security and transactions.

• REST: Simpler, more flexible, stateless, uses standard HTTP methods, typically uses JSON or XML for data exchange. Better suited for web applications and microservices.
• SOAP: More complex, stateful (can be), uses XML for messaging and data, often uses WS-* specifications for advanced features. Better suited for enterprise applications requiring robust transaction management and security.

Think of REST as sending a postcard – simple and informal. SOAP is like sending a registered letter – more formal, secure, and trackable, but with more overhead.

REST APIs primarily use standard HTTP methods to perform actions on resources. These methods clearly define the intention of the request:

• GET: Retrieves a resource.
• POST: Creates a new resource.
• PUT: Updates an existing resource.
• PATCH: Partially updates an existing resource.
• DELETE: Deletes a resource.

For example, a GET /users/123 request would retrieve information about user with ID 123, while a POST /users request would create a new user. Using the correct method improves API clarity and understanding.

API authentication verifies the identity of the client, while authorization determines what the client is allowed to do. Several methods exist:

• API Keys: Simple strings included in request headers. Easy to implement but less secure.
• OAuth 2.0: A widely used authorization framework granting delegated access to resources. Allows for fine-grained control and protects user credentials.
• JWT (JSON Web Tokens): Self-contained tokens containing user information and claims. Reduces the need for frequent authentication requests.
• Basic Authentication: Sending username and password in the request header (Base64 encoded). Simple but insecure unless HTTPS is used.

For example, an e-commerce API might use OAuth 2.0 to let users authorize an app to access their order information without revealing their password. Choosing the right method depends on security requirements and complexity.

API rate limiting controls the number of requests a client can make within a specific time frame. This prevents abuse, denial-of-service attacks, and ensures fair resource allocation for all users. It’s implemented using different strategies:

• Request count: Limiting the total number of requests within a time window (e.g., 100 requests per hour).
• Time window: Limiting the rate at which requests can be made (e.g., no more than 10 requests per minute).
• Sliding window: Allows bursts of activity by measuring requests over a sliding time window.
• Token bucket: Allows a controlled flow of tokens that authorize requests.

If a client exceeds the limit, the API typically returns an error code (e.g., 429 Too Many Requests). This protects the server from overload and ensures a positive experience for all users.

Different API design styles cater to various needs and architectures. Microservices, for instance, are a popular approach.

• Microservices: Breaking down a large application into smaller, independent services. Each service has its own API, enabling independent development, deployment, and scaling. Imagine a large online store—each service might handle orders, inventory, customer profiles separately.
• Monolithic: The traditional approach, where all functionalities are combined in a single application. Simpler to develop initially but harder to scale and maintain as it grows.
• GraphQL: A query language for APIs that allows clients to request only the data they need, enhancing efficiency and reducing over-fetching.

Choosing the right design style depends on factors like project size, scalability requirements, team structure and technology expertise. Microservices offer flexibility and scalability but require more complex management.

An API gateway acts as a reverse proxy, sitting in front of multiple backend APIs. It handles various tasks, improving efficiency and security:

• Request Routing: Directing requests to the appropriate backend service.
• Authentication and Authorization: Centralizing security checks.
• Rate Limiting: Managing requests to protect backend services.
• Transformation: Modifying requests or responses to match the needs of clients or services.
• Monitoring and Logging: Providing insights into API usage and performance.

Think of it as a concierge in a large hotel, managing guest requests and directing them to the right services. Benefits include improved security, simplified development, better performance, and enhanced scalability.

API versioning is crucial for maintaining backward compatibility while allowing for evolution and improvement of your API. Imagine building a house – you wouldn’t want to change the foundation every time you wanted to add a room! We use versioning to avoid breaking existing integrations.

There are several approaches:

• URI Versioning: This is the most common method. You include the version number directly in the API endpoint URL. For example: /v1/users, /v2/users. This is simple and easy to understand.
• Request Header Versioning: The version is specified in a custom HTTP header, like X-API-Version: 2. This allows for more flexibility, as you don’t need to change URLs. It also helps maintain cleaner URLs.
• Content Negotiation: You use the Accept header to specify the desired version using a custom media type, like application/vnd.yourcompany.v2+json. This is more complex to implement but can lead to elegant solutions.
• Query Parameter Versioning: Adding the version as a query parameter, for example: /users?version=2. While simple, it’s generally less preferred than URI versioning as it can clutter URLs and is not easily discoverable by tools.

Choosing the right method depends on factors like your API’s architecture and the level of complexity you’re willing to manage. I often prefer URI versioning for its simplicity and clarity, especially in early stages of development.

API documentation is the single most important aspect of a successful API. Think of it as the instruction manual for developers using your API. Poor documentation leads to frustration, wasted time, and ultimately, fewer users. Best practices include:

• Clear and Concise Language: Avoid jargon and technical terms unless absolutely necessary. Assume the user has a basic understanding of APIs but may not be familiar with your specific system.
• Comprehensive Examples: Include plenty of code examples in multiple programming languages (e.g., Python, JavaScript, Java). Show how to make requests, handle responses, and handle errors.
• Interactive Documentation: Tools like Swagger/OpenAPI allow for interactive documentation, enabling developers to test the API directly within the documentation itself. This provides a hands-on experience.
• Up-to-date Information: Keep your documentation synchronized with your API’s codebase. Version control integration is crucial here.
• Error Handling Details: Specify all potential error codes, their meanings, and how developers should handle them. Providing helpful suggestions in case of error is critical.
• Authentication and Authorization: Clearly document how users should authenticate and authorize their requests. This will greatly assist developers in integrating the API.

I always aim to create documentation that is not only technically sound but also user-friendly. Imagine trying to assemble IKEA furniture without instructions – it would be a nightmare!

API testing is a critical process to ensure API quality and stability. I’ve extensively used various tools and strategies. My approach involves a multi-layered strategy:

• Unit Testing: I use unit tests to verify individual API endpoints and functions, ensuring they behave as expected under different inputs.
• Integration Testing: These tests cover interactions between different API components and external systems.
• End-to-End Testing: I use end-to-end tests to simulate real-world usage scenarios, covering the entire API workflow from start to finish.

Tools I’ve used include:

• Postman: For manual testing, creating collections and environments for managing test cases, and generating automated tests. Excellent for exploratory testing and debugging.
• REST-Assured (Java): A powerful library for automating API tests in Java projects. Allows for concise and readable test code.
• pytest (Python): A versatile framework for writing automated tests in Python. Highly flexible and customizable.
• JMeter: A performance testing tool used for load testing and stress testing API endpoints to identify bottlenecks.

In a recent project, I used Postman extensively for exploratory testing during development, then migrated key test cases to REST-Assured for automated regression testing within our CI/CD pipeline. This ensured quick feedback and high-quality deliverables.

Debugging API integration issues can be challenging, but a systematic approach is key. My process typically involves:

1. Reproduce the Issue: First, I need to reliably reproduce the problem. This may involve running test cases or recreating the exact steps reported by users.
2. Check Logs: Review API logs on both the client and server sides. Logs provide valuable clues about errors, request parameters, and response codes.
3. Inspect Network Traffic: Tools like browser developer tools or network monitoring software can capture the actual HTTP requests and responses. This allows to see what data is exchanged and identify discrepancies.
4. Validate Request Data: Ensure the request data sent by the client matches the expected format and data type defined in the API documentation. Missing or incorrect parameters are common culprits.
5. Examine Response Codes and Messages: HTTP response codes (e.g., 400 Bad Request, 500 Internal Server Error) give important hints about the nature of the problem.
6. Use Debugging Tools: If the issue is server-side, I’ll use debugging tools like debuggers within the IDE or logging frameworks to step through code execution. This is great for identifying logical errors and unexpected behavior.
7. Check External Dependencies: If the API interacts with databases or other external services, ensure those services are running correctly and available.

I’ve found that a combination of careful logging and good use of debugging tools helps pinpoint the root cause of API integration issues much more quickly.

API security is paramount. Some common vulnerabilities include:

• SQL Injection: Malicious SQL code injected into API requests can compromise database integrity. Parameterized queries and input validation are essential mitigations.
• Cross-Site Scripting (XSS): Injecting malicious scripts into API responses can affect the user’s browser and potentially steal session cookies. Output encoding and content security policies (CSP) are key countermeasures.
• Cross-Site Request Forgery (CSRF): Tricking a user into performing unwanted actions on a website through the API. Using CSRF tokens in the requests provides a good defense.
• Broken Authentication and Session Management: Weak or insecure authentication mechanisms can lead to unauthorized access. Strong passwords, multi-factor authentication, and secure session management practices are necessary.
• Sensitive Data Exposure: Exposing sensitive data like API keys, passwords, or user data without proper protection. Secure storage and encryption methods should be employed.
• Lack of Rate Limiting: Failing to limit the number of requests from a single source can lead to denial-of-service (DoS) attacks. Implementing robust rate limiting is crucial.

Mitigating these requires a holistic approach combining secure coding practices, input validation, authentication mechanisms, encryption, and regular security audits. It’s like protecting a valuable asset—multiple layers of security are needed.

I’ve worked with various API protocols, each with its own strengths and weaknesses:

• REST (Representational State Transfer): The most common protocol, using standard HTTP methods (GET, POST, PUT, DELETE). It’s relatively simple to implement and widely understood.
• gRPC (Google Remote Procedure Call): A high-performance, open-source universal RPC framework. It uses Protocol Buffers for efficient data serialization and is ideal for internal microservices communication where performance is critical. It’s more complex than REST, but offers significant performance advantages.
• WebSockets: Enable bidirectional, real-time communication between client and server. Excellent for applications requiring real-time updates, such as chat applications or stock tickers. They require a different architecture than RESTful APIs.

Choosing the right protocol depends entirely on the specific application requirements. For instance, I’d choose gRPC for a high-throughput microservice architecture, REST for a public API with many different clients, and WebSockets for a real-time collaborative application.

API monitoring and logging are essential for ensuring API availability, performance, and security. Effective monitoring provides real-time insights into API health, performance, and error rates.

My approach involves:

• Logging: Detailed logs help track requests, responses, and errors. I’ve used tools like ELK stack (Elasticsearch, Logstash, Kibana) for centralized log management and analysis.
• Metrics: Monitoring key metrics like request latency, throughput, and error rates gives a comprehensive picture of API performance. Tools like Prometheus and Grafana are invaluable here.
• Alerting: Setting up alerts for critical events (e.g., high error rates, slow response times, or API downtime) enables proactive issue resolution.
• Tracing: Distributed tracing tools help track requests across multiple services and pinpoint bottlenecks. Tools like Jaeger or Zipkin are useful for this.

In a previous role, we implemented a comprehensive monitoring system using Prometheus and Grafana, providing dashboards that visualized key API metrics. This allowed us to detect and address performance issues before they affected end-users. Real-time monitoring is like having a flight control tower for your APIs – ensuring smooth operation and avoiding crashes!

Robust error handling is paramount in API development. It ensures a smooth user experience and aids in debugging. My approach involves a multi-layered strategy. First, I implement comprehensive exception handling within the code itself, using try-catch blocks (or equivalent mechanisms depending on the language) to gracefully handle predictable errors like invalid input or database connection issues. This prevents the API from crashing and allows for controlled responses.

Secondly, I use appropriate HTTP status codes to communicate the nature of errors to the client. For instance, a 400 Bad Request indicates client-side issues, while a 500 Internal Server Error signals problems on the server. Each status code is accompanied by a JSON response body containing a user-friendly error message and potentially a unique error code for easier tracking and debugging. Detailed logging is crucial; I ensure that all exceptions are logged with timestamps, stack traces, and relevant context, facilitating root cause analysis. Finally, I employ centralized error handling mechanisms, such as custom exception classes and middleware (in frameworks like Node.js Express or similar), to streamline error management across the entire API.

For example, if a user submits a form with missing required fields, the API will return a 400 Bad Request with a JSON response like { "error": "Missing required fields", "fields": ["name", "email"] }. This clear and informative response helps the client identify and correct the issue immediately. In a real-world scenario, I’ve used this approach in a large e-commerce API, where consistent error handling ensured reliable operation and minimized disruptions to the user experience.

API mocking involves creating simulated versions of external APIs or services. This is incredibly useful during development, testing, and even during integration with third-party systems that might be unavailable or under development. Think of it like creating a stand-in actor for a movie role before the actual actor is cast; it allows the rest of the production to continue.

The benefits of API mocking are numerous: it allows developers to work independently of external dependencies, speeding up development cycles. It enables comprehensive testing of API interactions without the need for a fully functional external system, which could be unreliable or expensive to access. By mimicking various scenarios, including successful and failed responses, developers can thoroughly test the resilience and error-handling capabilities of their API. Tools like WireMock, Mountebank, and even simple JSON files can be used for creating mock APIs.

In one project, we used a mocking framework to simulate a payment gateway API. This allowed our development team to build and test the e-commerce checkout flow before the payment gateway was fully integrated. This saved us considerable time and allowed for parallel development, accelerating our project timeline.

API performance optimization is a continuous process involving profiling, identifying bottlenecks, and applying targeted improvements. My experience includes using various techniques to enhance API speed and scalability. Profiling tools like New Relic or similar help pinpoint performance bottlenecks – perhaps a slow database query, inefficient algorithms, or network latency.

Common strategies I employ include:

• Database optimization: Indexing databases efficiently, optimizing queries, and using caching mechanisms like Redis or Memcached significantly reduces database access times.
• Caching: Caching frequently accessed data in memory (e.g., using in-memory caches) reduces the load on the database and improves response times dramatically.
• Asynchronous processing: Using message queues (like RabbitMQ or Kafka) for tasks like sending emails or processing large datasets allows the API to respond quickly without blocking the main thread.
• Load balancing: Distributing traffic across multiple servers ensures that no single server is overloaded, enhancing the system’s resilience and scalability.
• Code optimization: Efficient algorithms and data structures, minimizing unnecessary calculations and database calls, directly impact performance.

In a previous project, we improved the API response time by 70% by implementing database caching and optimizing a crucial database query that was responsible for a significant bottleneck. The result was a smoother user experience and better overall system performance.

Designing for scalability and maintainability requires a well-thought-out architecture and adherence to best practices. I focus on several key aspects:

• Microservices architecture: Breaking down the API into smaller, independent services improves scalability and simplifies maintenance. Changes to one service don’t affect others.
• Modular design: Designing the API with clearly defined modules and components enhances code organization and makes future modifications less disruptive.
• Versioning: Implementing a robust versioning strategy (e.g., using URI versioning or header-based versioning) allows for gradual updates and minimizes disruption to existing clients.
• Automated testing: Comprehensive unit, integration, and end-to-end tests ensure that changes don’t introduce new bugs and maintain the API’s functionality. Continuous integration and continuous deployment (CI/CD) pipelines automate the build, test, and deployment process, streamlining development and ensuring rapid releases.
• Documentation: Clear and well-maintained documentation is vital for onboarding new team members and ensuring that everyone understands the API’s functionality and architecture.

For example, in a large-scale project, we migrated from a monolithic API architecture to a microservices architecture. This allowed us to scale individual services independently based on their needs, improving performance and making updates less risky. This also significantly improved maintainability, allowing for smaller, more focused teams to manage different parts of the system.

API contract testing focuses on verifying that the API’s behavior aligns with its defined contract. This contract usually takes the form of an OpenAPI/Swagger specification or a similar format. The idea is to ensure that the provider (the API’s creator) and the consumer (the application using the API) have a shared understanding of how the API will behave.

I use contract testing extensively to prevent integration issues. The process typically involves generating test cases from the API contract (e.g., using tools like Pact) and running these tests on both the provider and consumer sides. This ensures that any changes to the API don’t unexpectedly break applications that depend on it. By catching integration problems early in the development cycle, contract testing saves time, reduces costs, and prevents disruptions.

In a real-world example, I used Pact to test an integration between an e-commerce platform and a payment gateway. The contract defined the expected requests and responses for payment processing. By running Pact tests on both the platform and the payment gateway, we ensured that any changes to either system wouldn’t cause integration failures. This proactive approach greatly simplified the integration process and guaranteed a stable system.

OpenAPI/Swagger is a widely used specification for describing RESTful APIs. It uses a standardized format (typically YAML or JSON) to define API endpoints, request and response formats, authentication methods, and other relevant details. This specification serves as a contract between the API provider and consumers.

I use OpenAPI/Swagger throughout the API lifecycle. First, it guides the design of the API, ensuring consistency and clarity. Then, it’s used to generate client SDKs and server stubs, automating parts of the development process. Additionally, it’s a crucial component for API documentation, providing a machine-readable and user-friendly way to describe the API’s functionality. Finally, tools can use the OpenAPI specification to perform automated testing and validation, ensuring that the API adheres to its design.

In one project, we used Swagger to define the API contract, generate client SDKs for various platforms, and produce interactive API documentation. This improved team collaboration, reduced development time, and provided consistent documentation for all users of the API.

Data transformations are frequently necessary in API integrations because different systems often use different data formats and structures. These transformations involve converting data from one format to another, enriching data with additional information, or cleaning and validating data.

My approach involves using tools and techniques appropriate to the context. For simple transformations, I might use built-in functions within the programming language (e.g., JSON parsing and manipulation in JavaScript or Python). For more complex scenarios, I might use dedicated data transformation tools like Apache Camel or ETL (Extract, Transform, Load) frameworks like Informatica PowerCenter. Mapping tools can help visually represent the data transformations required, making the process clearer and more maintainable.

Consider an integration where one system uses XML and another uses JSON. I’d use a transformation process (perhaps a script or a dedicated tool) to convert the XML data to JSON before sending it to the JSON-based system. Data validation would also be crucial to ensure data quality and prevent errors. In a project involving the integration of various data sources into a central data warehouse, I leveraged an ETL tool to perform data cleaning, transformation, and loading. This tool allowed for handling complex data transformations efficiently and managing data quality throughout the process.

My preferred tools for API development depend heavily on the project’s specific needs and the overall architecture. However, I consistently leverage a core set of technologies. For designing and documenting APIs, I rely on Swagger/OpenAPI, which provides a standardized way to define and visualize the API’s functionality. This not only aids in collaboration but also generates client SDKs and server stubs, saving significant development time.

For the backend, my go-to languages are Python (with frameworks like Flask or FastAPI) and Node.js (with Express.js). Both offer excellent performance and robust ecosystems of libraries for tasks like database interaction and authentication. For database interaction, ORMs like SQLAlchemy (Python) or TypeORM (Node.js) streamline database operations.

On the testing front, I use tools like Postman for manual testing and integration, and frameworks like pytest (Python) or Jest (Node.js) for automated unit and integration testing. Finally, for deployment and infrastructure management, I often use Docker for containerization and Kubernetes for orchestration, ensuring scalability and consistency across different environments.

My experience with CI/CD for APIs is extensive. I’ve implemented and managed CI/CD pipelines using various tools, including Jenkins, GitLab CI, and GitHub Actions. The key to effective API CI/CD is automation at every stage. This starts with automated testing—unit tests, integration tests, and end-to-end tests—to ensure code quality and prevent regressions. These tests are typically integrated into the build process.

After successful testing, the pipeline automatically builds the API, creates a Docker image (if using containers), and deploys the image to a staging environment for further testing and validation. This often involves automated deployment tools and infrastructure-as-code practices using tools like Terraform or Ansible. Once approved in staging, the pipeline seamlessly deploys the API to production, typically using blue/green deployment or canary deployments to minimize downtime and risk. Monitoring tools are integrated throughout the process to track performance and identify any issues post-deployment.

A typical pipeline might look something like this (simplified): git push -> Automated Tests -> Build -> Docker Image Creation -> Deploy to Staging -> Manual Testing & Approval -> Deploy to Production -> Monitoring

Ensuring API security in a microservices architecture requires a multi-layered approach. The decentralized nature of microservices means security needs to be built into each service individually, as well as implemented at the overall architecture level.

Authentication and Authorization: Each microservice should implement robust authentication (verifying the identity of the user or service) and authorization (controlling what actions the authenticated entity can perform). OAuth 2.0 and JWT (JSON Web Tokens) are popular choices for authentication. Role-based access control (RBAC) is often used for authorization.

API Gateways: An API gateway acts as a central point of entry for all API requests. It handles tasks like authentication, authorization, rate limiting, and request transformation. This centralizes security policies and simplifies management.

Input Validation and Sanitization: All input data should be thoroughly validated and sanitized to prevent injection attacks (SQL injection, cross-site scripting, etc.).

Secrets Management: Sensitive information like API keys and database credentials should be stored securely using a dedicated secrets management system, preventing hardcoding of credentials in the code.

Monitoring and Logging: Comprehensive logging and monitoring are crucial for identifying and responding to security incidents promptly. This allows for auditing and tracing suspicious activities.

Communication Security: All communication between services should be encrypted, typically using HTTPS.

My experience encompasses a range of database technologies in the context of API development, choosing the right database is crucial for performance and scalability. I’ve worked extensively with relational databases like PostgreSQL and MySQL for structured data, and NoSQL databases like MongoDB and Cassandra for unstructured or semi-structured data. The choice depends on the specific needs of the API.

For instance, if the API involves complex relationships between data, a relational database like PostgreSQL with its robust ACID properties is a good choice. For applications requiring high scalability and flexibility in data modeling, NoSQL databases like MongoDB are more suitable. I’ve also had experience with graph databases like Neo4j for applications involving complex relationships. In addition to database selection, I’m familiar with database optimization techniques to improve query performance and ensure the API remains responsive.

I usually use ORMs (Object-Relational Mappers) to interact with databases, abstracting away the complexities of SQL queries. This makes the code cleaner, more maintainable, and portable across different database systems.

Handling different data formats in APIs is essential for interoperability. The most common formats are JSON and XML, although others exist. My approach focuses on flexibility and maintainability.

I typically leverage libraries and frameworks that provide built-in support for these formats. For instance, in Python, the json library provides easy ways to encode and decode JSON data. Similarly, libraries exist for XML parsing and manipulation.

For ensuring flexibility, I often design APIs to be content-negotiation-aware. This means the API can respond in different formats (e.g., JSON or XML) depending on the client’s request. The client specifies the desired format through the Accept header in the HTTP request. For example, a client requesting Accept: application/json will receive a JSON response, while Accept: application/xml will result in an XML response. The API should gracefully handle requests for unsupported formats and return an appropriate error response.

Serverless architectures have significantly impacted API development, offering several advantages. The core idea is to offload the management of servers to a cloud provider, allowing developers to focus solely on code.

Benefits: Cost-effectiveness (paying only for compute time used), scalability (automatically scaling up or down based on demand), reduced operational overhead (no server management), and faster deployment cycles.

Impact on API Development: Serverless functions (like AWS Lambda, Azure Functions, or Google Cloud Functions) are well-suited for building APIs, especially microservices. Each function can handle a specific task or part of the API workflow. This allows for independent deployment and scaling of individual API components.

Challenges: Cold starts (initial delays in function execution), vendor lock-in (dependency on the specific cloud provider), and debugging complexities (debugging across multiple functions).

Overall, serverless is a powerful approach for API development, particularly for event-driven APIs or APIs with fluctuating demand. However, careful consideration of its limitations is crucial for successful implementation.

One challenging integration involved connecting our internal CRM system (a legacy system with a poorly documented API) to a new e-commerce platform. The legacy CRM API was unreliable, lacked proper error handling, and used an outdated data format (a custom XML). The e-commerce platform expected data in JSON format.

Solution: I implemented a robust intermediary service acting as a translator and error handler. This service received data requests from the e-commerce platform, translated them into the format required by the legacy CRM API, handled potential errors (timeouts, malformed responses), and re-formatted the response from the CRM into JSON for the e-commerce platform.

Steps: 1. Created a separate microservice using Python and the `requests` library to interact with the legacy CRM API. 2. Implemented comprehensive error handling and retry mechanisms to address API unreliability. 3. Developed custom XML and JSON parsers and transformers. 4. Implemented logging and monitoring to track API interactions and identify potential issues. 5. Thoroughly tested the intermediary service before deploying it to production.

This solution mitigated the risks associated with directly integrating with the unreliable CRM API and ensured the seamless functioning of the e-commerce platform. The intermediary service improved reliability and maintained data consistency between the two systems.