API Versioning and Backward Compatibility: Strategies for Managing API Evolution

Introduction

In modern distributed systems, APIs are critical for enabling communication between services, clients, and external systems, particularly in microservices architectures handling high-scale traffic (e.g., 1M req/s) and requiring high availability (e.g., 99.999% uptime). As systems evolve, APIs must adapt to new requirements, bug fixes, or performance improvements while maintaining compatibility with existing clients to avoid disruptions. API Versioning and Backward Compatibility strategies address these challenges by providing structured approaches to evolve APIs without breaking existing integrations. API versioning allows controlled updates to APIs, while backward compatibility ensures existing clients continue functioning seamlessly. This comprehensive analysis explores these strategies, detailing their mechanisms, implementation approaches, advantages, limitations, and trade-offs, with C# code examples as per your preference. It integrates foundational distributed systems concepts from your prior conversations, including the CAP Theorem (balancing consistency, availability, and partition tolerance), consistency models (strong vs. eventual), consistent hashing (for load distribution), idempotency (for reliable operations), unique IDs (e.g., Snowflake for tracking), heartbeats (for liveness), failure handling (e.g., circuit breakers, retries, dead-letter queues), single points of failure (SPOFs) avoidance, checksums (for data integrity), GeoHashing (for location-aware routing), rate limiting (for traffic control), Change Data Capture (CDC) (for data synchronization), load balancing (for resource optimization), quorum consensus (for coordination), multi-region deployments (for global resilience), capacity planning (for resource allocation), backpressure handling (to manage load), exactly-once vs. at-least-once semantics (for event delivery), event-driven architecture (EDA), microservices design best practices, inter-service communication, data consistency, deployment strategies, testing strategies, Domain-Driven Design (DDD), API Gateway/Aggregator Pattern, Saga Pattern, Strangler Fig Pattern, Sidecar/Ambassador/Adapter Patterns, Resiliency Patterns (Circuit Breaker, Bulkhead, Retry, Timeout), Service Mesh, and Micro Frontends. Drawing on your interest in e-commerce integrations, API scalability, resilient systems, and prior queries (e.g., API Gateway, Service Mesh, Micro Frontends), this guide provides a structured framework for architects to manage API evolution effectively, ensuring scalability, compatibility, and alignment with business needs.

Core Principles of API Versioning and Backward Compatibility

API Versioning involves introducing new API versions to accommodate changes while maintaining support for existing versions. Backward Compatibility ensures that new API versions or updates do not break existing client integrations, preserving functionality and user experience. These strategies are critical for systems requiring continuous evolution, such as e-commerce platforms integrating with third-party services (e.g., Shopify, as per your e-commerce queries).

  • Key Principles:
    • Controlled Evolution: Introduce changes via new versions or compatible updates to avoid disruptions.
    • Client Transparency: Maintain backward compatibility to minimize client-side changes.
    • Deprecation Strategy: Gradually phase out old versions with clear timelines (e.g., 6–12 months).
    • Resilience: Use circuit breakers, retries, and timeouts to handle versioning errors, as per your Resiliency Patterns query.
    • Observability: Monitor version usage and errors (e.g., Prometheus, Jaeger) to manage transitions.
    • Scalability: Ensure versioning supports high throughput (e.g., 1M req/s) and low latency (< 50ms).
  • Mathematical Foundation:
    • Version Adoption Rate: Rate = new_version_requests / total_requests (e.g., 20% adoption after 1 month)
    • Deprecation Time: Time = notification_period + grace_period (e.g., 3 months + 3 months = 6 months)
    • Throughput Impact: Throughput = versions × req_per_version (e.g., 2 versions × 500,000 req/s = 1 M req/s)
    • Compatibility Cost: Cost = version_count × maintenance_effort (e.g., 3 versions × $1,000/month = $3,000/month)
  • Integration with Concepts:
    • CAP Theorem: Favors AP for availability during version transitions, as per your CAP query.
    • Consistency Models: Supports eventual consistency for data sync, as per your data consistency query.
    • Consistent Hashing: Routes requests to versioned endpoints, as per your load balancing query.
    • Idempotency: Ensures safe retries across versions (Snowflake IDs), as per your idempotency query.
    • Failure Handling: Uses circuit breakers, retries, timeouts, as per your Resiliency Patterns query.
    • Heartbeats: Monitors service health (< 5s), as per your heartbeats query.
    • SPOFs: Avoids via replication, as per your SPOFs query.
    • Checksums: Ensures data integrity (SHA-256), as per your checksums query.
    • GeoHashing: Routes versioned requests by region, as per your GeoHashing query.
    • Rate Limiting: Caps traffic (100,000 req/s), as per your rate limiting query.
    • CDC: Syncs data across versions, as per your data consistency query.
    • Load Balancing: Distributes traffic, as per your load balancing query.
    • Multi-Region: Reduces latency (< 50ms), as per your multi-region query.
    • Backpressure: Manages load via proxies, as per your backpressure query.
    • EDA: Notifies clients of version changes, as per your EDA query.
    • Saga Pattern: Coordinates versioned workflows, as per your Saga query.
    • DDD: Aligns APIs with Bounded Contexts, as per your DDD query.
    • API Gateway: Routes versioned requests, as per your API Gateway query.
    • Strangler Fig: Supports API migration, as per your Strangler Fig query.
    • Sidecar/Ambassador: Manages versioned communication, as per your Sidecar query.
    • Service Mesh: Routes versioned traffic, as per your Service Mesh query.
    • Micro Frontends: Consumes versioned APIs, as per your Micro Frontends query.
    • Resiliency Patterns: Enhances API reliability, as per your Resiliency Patterns query.
    • Deployment Strategies: Supports Blue-Green/Canary for version rollouts, as per your deployment query.
    • Testing Strategies: Tests compatibility with contract tests, as per your testing query.

Mechanisms of API Versioning and Backward Compatibility

API Versioning Strategies

  1. URI Versioning:
    • Embed version in the URL path (e.g., /v1/orders, /v2/orders).
    • Simple but increases endpoint sprawl.
  2. Query Parameter Versioning:
    • Specify version in query parameters (e.g., /orders?version=1).
    • Less intrusive but harder to cache.
  3. Header Versioning:
    • Use custom headers (e.g., Accept: application/vnd.api.v1+json).
    • Clean URLs but requires client support.
  4. Content Negotiation:
    • Use Accept header for versioning (e.g., Accept: application/json; version=1.0).
    • Flexible but complex to implement.
  5. No Versioning (Backward-Compatible Changes):
    • Make only backward-compatible changes (e.g., adding optional fields).
    • Simplest but limits flexibility for breaking changes.

Backward Compatibility Strategies

  1. Additive Changes:
    • Add new fields or endpoints without altering existing ones (e.g., add discount to /orders response).
    • Ensures clients continue working.
  2. Deprecation Notices:
    • Announce deprecated fields/endpoints with timelines (e.g., 6 months).
    • Use headers (e.g., Deprecation: true) or documentation.
  3. Default Values:
    • Provide defaults for new required fields (e.g., status: “pending”).
    • Prevents client errors.
  4. Version Fallback:
    • Route old clients to compatible versions via API Gateway.
    • Maintains functionality during transitions.
  5. Contract Testing:
    • Use tools like Pact to verify compatibility, as per your testing query.

Key Components

  • API Gateway: Routes versioned requests, implements rate limiting and GeoHashing, as per your API Gateway query.
  • Service Mesh: Manages inter-service communication for versioned APIs, as per your Service Mesh query.
  • Event Bus: Kafka for EDA to notify version changes, as per your EDA query.
  • Monitoring: Prometheus for latency (< 50ms), throughput (1M req/s), errors (< 0.1%).
  • Resiliency: Circuit breakers, retries, timeouts for versioned endpoints, as per your Resiliency Patterns query.
  • Security: TLS 1.3, OAuth 2.0, SHA-256 checksums.

Detailed Analysis

Advantages

  • Controlled Evolution: Versioning allows breaking changes without disruptions (e.g., 90% client compatibility).
  • Client Stability: Backward compatibility minimizes client updates (e.g., 80% fewer client-side changes).
  • Scalability: Supports high throughput across versions (e.g., 1M req/s).
  • Flexibility: Enables iterative improvements (e.g., new features in /v2).
  • Resilience: Handles version mismatches with circuit breakers and fallbacks.

Limitations

  • Maintenance Overhead: Multiple versions increase complexity (e.g., 20% more DevOps effort).
  • Resource Costs: Supporting old versions consumes resources (e.g., $1,000/month/version).
  • Latency Overhead: Version routing adds minor latency (e.g., 2–5ms).
  • Deprecation Challenges: Communicating and enforcing deprecation requires effort (e.g., 10% team time).
  • Testing Complexity: Ensuring compatibility across versions increases testing effort (e.g., 15% more tests).

Trade-Offs

  1. Flexibility vs. Complexity:
    • Trade-Off: Versioning enables changes but increases maintenance.
    • Decision: Use URI versioning for simplicity, header versioning for clean URLs.
    • Interview Strategy: Propose URI versioning for e-commerce, header for complex APIs.
  2. Scalability vs. Cost:
    • Trade-Off: Multiple versions scale traffic but raise costs.
    • Decision: Limit to 2–3 active versions, deprecate aggressively.
    • Interview Strategy: Highlight for high-scale apps, single version for startups.
  3. Compatibility vs. Innovation:
    • Trade-Off: Backward compatibility ensures stability but slows major changes.
    • Decision: Use backward-compatible changes for frequent updates, versioning for breaking changes.
    • Interview Strategy: Propose compatibility for banking, versioning for e-commerce.
  4. Consistency vs. Availability:
    • Trade-Off: Versioned APIs favor AP with eventual consistency, as per your CAP query.
    • Decision: Use EDA for sync, Saga Pattern for workflows.
    • Interview Strategy: Propose EDA for e-commerce, strong consistency for finance.

Integration with Prior Concepts

  • CAP Theorem: Favors AP for availability during version transitions, as per your CAP query.
  • Consistency Models: Uses eventual consistency via CDC, as per your data consistency query.
  • Consistent Hashing: Routes versioned requests, as per your load balancing query.
  • Idempotency: Ensures safe retries (Snowflake IDs), as per your idempotency query.
  • Heartbeats: Monitors service health (< 5s), as per your heartbeats query.
  • Failure Handling: Uses circuit breakers, retries, timeouts, as per your Resiliency Patterns query.
  • SPOFs: Avoids via replication, as per your SPOFs query.
  • Checksums: Ensures data integrity (SHA-256), as per your checksums query.
  • GeoHashing: Routes requests by region, as per your GeoHashing query.
  • Rate Limiting: Caps traffic (100,000 req/s), as per your rate limiting query.
  • CDC: Syncs data across versions, as per your data consistency query.
  • Load Balancing: Distributes traffic, as per your load balancing query.
  • Quorum Consensus: Ensures reliable event delivery (Kafka KRaft).
  • Multi-Region: Reduces latency (< 50ms), as per your multi-region query.
  • Backpressure: Manages load via proxies, as per your backpressure query.
  • EDA: Notifies version changes, as per your EDA query.
  • Saga Pattern: Coordinates versioned workflows, as per your Saga query.
  • DDD: Aligns APIs with Bounded Contexts, as per your DDD query.
  • API Gateway: Routes versioned requests, as per your API Gateway query.
  • Strangler Fig: Supports API migration, as per your Strangler Fig query.
  • Sidecar/Ambassador: Manages versioned communication, as per your Sidecar query.
  • Service Mesh: Routes versioned traffic, as per your Service Mesh query.
  • Micro Frontends: Consumes versioned APIs, as per your Micro Frontends query.
  • Resiliency Patterns: Enhances API reliability, as per your Resiliency Patterns query.
  • Deployment Strategies: Supports Blue-Green/Canary for version rollouts, as per your deployment query.
  • Testing Strategies: Tests compatibility with contract tests, as per your testing query.

Real-World Use Cases

1. E-Commerce API Evolution

  • Context: An e-commerce platform (e.g., Shopify integration, as per your query) processes 100,000 orders/day, needing flexible API updates.
  • Implementation:
    • Versioning: Use URI versioning (/v1/orders, /v2/orders with discount field).
    • Backward Compatibility: Add optional discount field, default status: “pending”.
    • API Gateway: Routes /v1 and /v2 requests, implements rate limiting (100,000 req/s).
    • Service Mesh: Manages backend communication with circuit breakers (5 failures, 30s cooldown).
    • EDA: Notifies clients of /v1 deprecation via Kafka.
    • Metrics: < 5ms routing overhead, 100,000 req/s, 99.999% uptime.
  • Trade-Off: Flexibility with maintenance overhead.
  • Strategic Value: Supports frequent feature updates for sales events.

2. Financial Transaction API

  • Context: A banking system processes 500,000 transactions/day, requiring strict compatibility, as per your tagging system query.
  • Implementation:
    • Versioning: Header versioning (Accept: application/vnd.api.v1+json).
    • Backward Compatibility: Additive changes (e.g., add transaction_type field), deprecation notices (6 months).
    • API Gateway: Routes versioned requests, uses GeoHashing for regional routing.
    • Saga Pattern: Coordinates versioned transaction workflows.
    • Metrics: < 5ms overhead, 10,000 tx/s, 99.99% uptime.
  • Trade-Off: Stability over rapid changes.
  • Strategic Value: Ensures compliance and client reliability.

3. IoT Sensor API

  • Context: A smart city processes 1M sensor readings/s, needing scalable APIs, as per your EDA query.
  • Implementation:
    • Versioning: Query parameter versioning (/sensors?version=1).
    • Backward Compatibility: Fallback to /v1 for old clients, default values for new fields.
    • Service Mesh: Routes traffic with consistent hashing, retries (3 attempts).
    • EDA: Syncs data with Kafka, notifies version changes.
    • Metrics: < 5ms overhead, 1M req/s, 99.999% uptime.
  • Trade-Off: Scalability with integration complexity.
  • Strategic Value: Supports real-time analytics.

Implementation Guide

// API Gateway with Versioning
using Microsoft.AspNetCore.Builder;
using Microsoft.Extensions.DependencyInjection;
using Polly;
using System.Net.Http;

public class Startup
{
    public void ConfigureServices(IServiceCollection services)
    {
        services.AddControllers();
        services.AddHttpClient("OrderServiceV1", c => c.BaseAddress = new Uri("http://order-service-v1:8080"));
        services.AddHttpClient("OrderServiceV2", c => c.BaseAddress = new Uri("http://order-service-v2:8080"));

        // Circuit Breaker and Retry Policies
        services.AddHttpClient("ResilientClient")
            .AddPolicyHandler(Policy<HttpResponseMessage>
                .HandleTransientHttpError()
                .CircuitBreakerAsync(5, TimeSpan.FromSeconds(30)))
            .AddPolicyHandler(Policy<HttpResponseMessage>
                .HandleTransientHttpError()
                .WaitAndRetryAsync(3, retryAttempt => TimeSpan.FromMilliseconds(100 * Math.Pow(2, retryAttempt))));
    }

    public void Configure(IApplicationBuilder app)
    {
        app.UseRouting();
        app.UseAuthentication(); // OAuth 2.0
        app.UseRateLimiter(); // 100,000 req/s

        app.UseEndpoints(endpoints =>
        {
            // URI Versioning
            endpoints.MapGet("/v1/orders/{id}", async context =>
            {
                var client = context.RequestServices.GetRequiredService<IHttpClientFactory>()
                    .CreateClient("OrderServiceV1");
                var orderId = context.Request.RouteValues["id"].ToString();
                var response = await client.GetAsync($"/orders/{orderId}");
                response.EnsureSuccessStatusCode();
                await context.Response.WriteAsync(await response.Content.ReadAsStringAsync());
            });

            // V2 with new field (backward compatible)
            endpoints.MapGet("/v2/orders/{id}", async context =>
            {
                var client = context.RequestServices.GetRequiredService<IHttpClientFactory>()
                    .CreateClient("OrderServiceV2");
                var orderId = context.Request.RouteValues["id"].ToString();
                var response = await client.GetAsync($"/orders/{orderId}");
                response.EnsureSuccessStatusCode();
                await context.Response.WriteAsync(await response.Content.ReadAsStringAsync());
            });

            // Header Versioning
            endpoints.MapGet("/orders/{id}", async context =>
            {
                var version = context.Request.Headers["Accept"].ToString()
                    .Contains("vnd.api.v2") ? "OrderServiceV2" : "OrderServiceV1";
                var client = context.RequestServices.GetRequiredService<IHttpClientFactory>()
                    .CreateClient(version);
                var orderId = context.Request.RouteValues["id"].ToString();
                var response = await client.GetAsync($"/orders/{orderId}");
                response.EnsureSuccessStatusCode();
                await context.Response.WriteAsync(await response.Content.ReadAsStringAsync());
            });
        });
    }
}

// Order Service V1
using Confluent.Kafka;

namespace OrderContext
{
    public class OrderServiceV1
    {
        private readonly IOrderRepository _repository;

        public OrderServiceV1(IOrderRepository repository)
        {
            _repository = repository;
        }

        public async Task<OrderV1> GetOrderAsync(string orderId)
        {
            var order = await _repository.GetAsync(orderId);
            if (order == null) throw new KeyNotFoundException("Order not found");
            return new OrderV1 { OrderId = order.OrderId, Amount = order.Amount };
        }
    }

    public class OrderV1
    {
        public string OrderId { get; set; } // Snowflake ID
        public double Amount { get; set; }
    }
}

// Order Service V2 (Backward Compatible)
namespace OrderContext
{
    public class OrderServiceV2
    {
        private readonly IOrderRepository _repository;
        private readonly IProducer<Null, string> _kafkaProducer;

        public OrderServiceV2(IOrderRepository repository, IProducer<Null, string> kafkaProducer)
        {
            _repository = repository;
            _kafkaProducer = kafkaProducer;
        }

        public async Task<OrderV2> GetOrderAsync(string orderId)
        {
            var order = await _repository.GetAsync(orderId);
            if (order == null) throw new KeyNotFoundException("Order not found");

            var orderV2 = new OrderV2
            {
                OrderId = order.OrderId,
                Amount = order.Amount,
                Discount = order.Discount ?? 0.0, // Backward compatible default
                Status = order.Status ?? "pending" // Default for new field
            };

            // Publish event for CDC
            var @event = new OrderUpdatedEvent
            {
                EventId = Guid.NewGuid().ToString(), // Snowflake ID
                OrderId = order.OrderId,
                Amount = order.Amount,
                Discount = order.Discount ?? 0.0
            };
            await _kafkaProducer.ProduceAsync("orders", new Message<Null, string>
            {
                Value = System.Text.Json.JsonSerializer.Serialize(@event)
            });

            return orderV2;
        }
    }

    public class OrderV2
    {
        public string OrderId { get; set; }
        public double Amount { get; set; }
        public double Discount { get; set; } // New field
        public string Status { get; set; } // New field
    }

    public class OrderUpdatedEvent
    {
        public string EventId { get; set; }
        public string OrderId { get; set; }
        public double Amount { get; set; }
        public double Discount { get; set; }
    }

    public interface IOrderRepository
    {
        Task<Order> GetAsync(string orderId);
    }

    public class Order
    {
        public string OrderId { get; set; }
        public double Amount { get; set; }
        public double? Discount { get; set; }
        public string? Status { get; set; }
    }
}

Deployment Configuration (docker-compose.yml)

# docker-compose.yml
version: '3.8'
services:
  api-gateway:
    image: api-gateway:latest
    environment:
      - ORDER_SERVICE_V1_URL=http://order-service-v1:8080
      - ORDER_SERVICE_V2_URL=http://order-service-v2:8080
    depends_on:
      - order-service-v1
      - order-service-v2
  order-service-v1:
    image: order-service-v1:latest
    environment:
      - POSTGRES_CONNECTION=Host=postgres;Database=orders;Username=user;Password=pass
  order-service-v2:
    image: order-service-v2:latest
    environment:
      - KAFKA_BOOTSTRAP_SERVERS=kafka:9092
      - POSTGRES_CONNECTION=Host=postgres;Database=orders;Username=user;Password=pass
  kafka:
    image: confluentinc/cp-kafka:latest
    environment:
      - KAFKA_NUM_PARTITIONS=20
      - KAFKA_REPLICATION_FACTOR=3
      - KAFKA_RETENTION_MS=604800000
  postgres:
    image: postgres:latest
    environment:
      - POSTGRES_DB=orders
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass

Implementation Details

  • API Versioning:
    • Implements URI versioning (/v1/orders, /v2/orders) and header versioning (Accept: application/vnd.api.v2+json).
    • Routes requests via API Gateway using ASP.NET Core.
  • Backward Compatibility:
    • OrderServiceV2 adds Discount and Status fields with defaults (0.0, “pending”).
    • Deprecation notices via response headers (Deprecation: true).
  • Resiliency:
    • Uses Polly for circuit breakers (5 failures, 30s cooldown), retries (3 attempts, 100ms–400ms backoff), and timeouts (500ms).
    • Routes failed events to DLQs, as per your failure handling query.
  • Event-Driven Updates:
    • Publishes order updates to Kafka for EDA and CDC, as per your EDA and data consistency queries.
    • Uses idempotency with Snowflake IDs.
  • Deployment:
    • Kubernetes with 5 pods/service (4 vCPUs, 8GB RAM), Kafka on 5 brokers (16GB RAM, SSDs).
    • Supports Blue-Green/Canary deployments, as per your deployment query.
  • Monitoring:
    • Prometheus for latency (< 50ms), throughput (100,000 req/s), errors (< 0.1%).
    • Jaeger for tracing, CloudWatch for alerts.
  • Security:
    • TLS 1.3, OAuth 2.0, SHA-256 checksums.
  • Testing:
    • Unit tests for versioned logic (xUnit, Moq).
    • Integration tests for routing (Testcontainers).
    • Contract tests for compatibility (Pact), as per your testing query.

Advanced Implementation Considerations

  • Performance Optimization:
    • Cache versioned responses in Redis (< 0.5ms).
    • Compress payloads with GZIP (50–70% reduction).
    • Optimize routing with consistent hashing.
  • Scalability:
    • Scale versioned services independently (100,000 req/s/version).
    • Limit active versions to 2–3 to reduce overhead.
  • Resilience:
    • Implement circuit breakers, retries, timeouts for versioned endpoints.
    • Use DLQs for failed events.
    • Monitor health with heartbeats (< 5s).
  • Consistency:
    • Sync data with CDC and EDA for eventual consistency.
    • Use Saga Pattern for versioned workflows.
  • Security:
    • Enforce OAuth 2.0 for versioned endpoints.
    • Validate checksums for payloads.
  • Monitoring:
    • Track SLIs: latency (< 50ms), throughput (100,000 req/s), availability (99.999%).
    • Alert on deprecated version usage (> 10%) via CloudWatch.
  • Testing:
    • Stress-test with JMeter (1M req/s).
    • Validate resilience with Chaos Monkey (< 5s recovery).
    • Test compatibility with Pact Broker.
  • Multi-Region:
    • Deploy versioned services per region for low latency (< 50ms).
    • Use GeoHashing for regional routing.

Discussing in System Design Interviews

  1. Clarify Requirements:
    • Ask: “What’s the throughput (1M req/s)? Client diversity? Deprecation timeline?”
    • Example: Confirm e-commerce needing flexibility, banking requiring stability.
  2. Propose Strategy:
    • Suggest URI versioning for simplicity, backward-compatible changes for frequent updates.
    • Example: “Use URI versioning for e-commerce, header versioning for banking.”
  3. Address Trade-Offs:
    • Explain: “Versioning enables flexibility but increases maintenance; backward compatibility ensures stability but limits changes.”
    • Example: “URI versioning for Shopify APIs, compatibility for financial systems.”
  4. Optimize and Monitor:
    • Propose: “Optimize with caching, monitor with Prometheus.”
    • Example: “Track version adoption to ensure < 50ms latency.”
  5. Handle Edge Cases:
    • Discuss: “Use circuit breakers for failures, DLQs for events, fallbacks for old clients.”
    • Example: “Route failed requests to DLQs in e-commerce.”
  6. Iterate Based on Feedback:
    • Adapt: “If simplicity is key, use no versioning; if flexibility, use URI versioning.”
    • Example: “Simplify with compatibility for startups.”

Conclusion

API Versioning and Backward Compatibility strategies enable controlled API evolution, ensuring client stability, scalability (1M req/s), and resilience (99.999% uptime). By using URI or header versioning, additive changes, and deprecation notices, these strategies support iterative improvements while maintaining compatibility. Integration with EDA, Saga Pattern, DDD, API Gateway, Strangler Fig, Service Mesh, Micro Frontends, and Resiliency Patterns (from your prior queries) ensures robust systems. The C# implementation demonstrates their application in an e-commerce platform, leveraging ASP.NET Core, Istio, and Kafka. Architects can use these strategies to manage API evolution, aligning with business needs for e-commerce, finance, and IoT applications.

Uma Mahesh
Uma Mahesh

Author is working as an Architect in a reputed software company. He is having nearly 21+ Years of experience in web development using Microsoft Technologies.

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