Advanced Golang Training – High-Performance Development Track

🐹 Golang + Next.js

Currently available in Islamabad

---

Go was designed at Google to solve one problem: building high-throughput network services that are fast to write, easy to maintain, and massively concurrent. It succeeded. Today Go powers the infrastructure of Docker, Kubernetes, Terraform, Prometheus, CockroachDB, Cloudflare, Uber, Dropbox, and thousands of production APIs that handle millions of requests per second. It is the language of cloud infrastructure — and increasingly, the language of choice for any team that needs backend performance without the complexity of Rust or the runtime overhead of Python and Node.js.

This course pairs Go's backend strengths with Next.js 15 on the frontend — covering the full stack from a single Go binary serving a REST and gRPC API to a TypeScript frontend consuming it. You will build production-ready services, not toy examples — with proper concurrency patterns, database access, observability, and deployment from day one.

💡 Why Go in 2025

---

• Go routines and channels make concurrency straightforward — handling 100,000 simultaneous connections on a single machine is a normal Go program, not an optimisation exercise
• Compiles to a single static binary with no runtime dependencies — the simplest possible deployment story for Docker and serverless
• Fast compilation — a large Go codebase builds in seconds, not minutes
• The language of cloud-native tooling — if you work with Kubernetes, Terraform, Prometheus, or any CNCF project, you are working with Go code
• gRPC was designed with Go as a first-class language — Go is the primary language for building microservices that communicate over gRPC
• Strong remote job market — Go engineers are consistently among the highest-paid backend developers on international platforms
• Simple, readable syntax — Go's explicit style makes codebases maintainable by teams of any size

📚 Module Breakdown

---

Week 1 — Phase 0: Go Fundamentals & Idiomatic Go

Go is a small language with a big philosophy. Its power comes not from features but from constraints — explicit error handling, no inheritance, no generics overuse, and a standard library that covers most of what you need. This phase builds the mental model correctly from the start.

1. Go toolchain: go install, go mod, go build, go test, go vet, gofmt, and golangci-lint
2. Modules and workspaces: go.mod, go.sum, dependency management, and multi-module workspaces
3. Types: primitive types, composite types (arrays, slices, maps, structs), pointers, and type aliases
4. Slices in depth: backing arrays, append, copy, slice tricks, and avoiding subtle bugs with shared backing arrays
5. Functions: multiple return values, named return values, variadic functions, and first-class functions
6. Interfaces: Go's implicit interface satisfaction, the empty interface (any), type assertions, and type switches
7. Structs and methods: value receivers vs pointer receivers — the rules and when to use each
8. Error handling: the error interface, wrapping errors with fmt.Errorf and %w, errors.Is and errors.As, and sentinel errors
9. Generics (Go 1.18+): type parameters, constraints, and writing generic functions and data structures — practical use without overengineering
10. Defer, panic, and recover: Go's mechanism for cleanup and controlled error recovery
11. Packages: structuring Go code, exported vs unexported identifiers, and package design principles
12. Testing: go test, table-driven tests, subtests, testify for assertions, and test coverage
13. Benchmarking: writing benchmark functions and profiling with go test -bench
14. The standard library tour: net/http, encoding/json, io, os, strings, strconv, time, context, and sync
15. Idiomatic Go: writing code that looks like Go — naming conventions, error wrapping patterns, and the principle of simplicity

Week 1–2 — Phase 1: Concurrency — Goroutines, Channels & the sync Package

Go's concurrency model is its most distinctive and powerful feature. Getting it right is what separates Go engineers from developers who happened to learn Go syntax. This phase goes deep — goroutines, channels, the sync package, and the patterns that production Go services are built on.

1. Goroutines: lightweight threads scheduled by the Go runtime — creating them, their cost, and the M:N scheduling model
2. The Go scheduler: GOMAXPROCS, OS threads, goroutine scheduling, and why goroutines are cheap
3. Channels: typed, synchronised communication between goroutines — unbuffered vs buffered channels
4. Channel directions: send-only and receive-only channels for safer function signatures
5. Select statement: waiting on multiple channels simultaneously — timeout patterns and non-blocking channel operations
6. Pipeline pattern: chaining goroutines with channels to build composable data processing pipelines
7. Fan-out and fan-in: distributing work across multiple goroutines and collecting results
8. Worker pool pattern: limiting concurrency to a fixed number of goroutines — the most common production pattern
9. Done channel and context cancellation: signalling goroutines to stop — the correct way to cancel work
10. sync.WaitGroup: waiting for a collection of goroutines to complete
11. sync.Mutex and sync.RWMutex: protecting shared state — when to use mutexes vs channels
12. sync.Once: lazy initialisation that runs exactly once regardless of concurrent access
13. sync.Map: the concurrent-safe map for specific access patterns
14. atomic operations: lock-free counters and flags with sync/atomic
15. Race detector: running go test -race to catch data races at test time
16. errgroup: running concurrent operations and collecting the first error elegantly
17. Context package in depth: WithCancel, WithTimeout, WithDeadline, and WithValue — propagating cancellation and request-scoped values through the call stack

Week 2 — Phase 2: REST API Development with Gin & Fiber

Both major Go web frameworks are covered — Gin for teams that want maturity and ecosystem size, Fiber for teams that need maximum throughput with an Express-like API.

Gin framework:

1. Gin architecture: router, context, handlers, and the middleware chain
2. Routing: static routes, path parameters, query parameters, route groups, and nested groups
3. Request binding: binding JSON, form data, query parameters, and path params to structs with validation tags
4. Validation with go-playground/validator: struct tags, custom validators, and error message formatting
5. Middleware: writing custom middleware, built-in middleware (logger, recovery, CORS), and middleware ordering
6. Response helpers: JSON, string, file, and streaming responses
7. Error handling: centralised error handling middleware and consistent error response format
8. gin.Context: request lifecycle, keys for passing data between middleware and handlers

Fiber framework:

1. Fiber architecture: built on fasthttp — why it is significantly faster than net/http-based frameworks
2. Fiber vs Gin: when to choose each — throughput benchmarks and the trade-offs
3. Routing, middleware, and request binding in Fiber — similarities and differences from Gin
4. Fiber's built-in middleware: rate limiter, cache, compress, CSRF, and keyauth

Production API patterns (framework-agnostic):

1. Project structure: the standard Go project layout — cmd, internal, pkg, and api directories
2. Dependency injection in Go: constructor injection, wire for compile-time DI, and fx for runtime DI
3. Configuration management: Viper for multi-source config (env vars, files, flags) with struct binding
4. Authentication: JWT with golang-jwt/jwt, refresh token rotation, and middleware-based route protection
5. Password hashing with bcrypt and Argon2id
6. Role-based access control: permission middleware and policy-based authorisation with Casbin
7. Request ID and correlation ID middleware: tracing requests across services
8. Graceful shutdown: draining in-flight requests and closing connections cleanly on SIGTERM
9. API versioning: URI versioning, header versioning, and managing multiple versions in one codebase
10. OpenAPI documentation: generating Swagger docs from Go code with swaggo/swag
11. Integration testing: httptest, test containers (testcontainers-go) for spinning up real PostgreSQL and Redis in tests

Week 2–3 — Phase 3: Database Layer — PostgreSQL, Redis & Beyond

Go has a clean, powerful database ecosystem. This phase covers raw SQL with sqlx, the ORM approach with GORM, compile-time query generation with sqlc, and caching with Redis.

1. database/sql: Go's standard database interface — connection pools, prepared statements, transactions, and row scanning
2. sqlx: extension of database/sql with struct scanning, named queries, and in-clause expansion
3. GORM: Go's most popular ORM — models, associations, migrations, hooks, scopes, and raw SQL fallback
4. sqlc: generating type-safe Go code from SQL queries — the compile-time approach to database access
5. Choosing between sqlx, GORM, and sqlc: a practical decision framework for different project types
6. Database migrations with golang-migrate: versioned, repeatable schema migrations from CLI and application code
7. Connection pool configuration: SetMaxOpenConns, SetMaxIdleConns, SetConnMaxLifetime — tuning for production workloads
8. Transactions in Go: Begin, Commit, Rollback, and writing transaction helper functions
9. Bulk inserts and batch operations: inserting thousands of records efficiently with PostgreSQL COPY
10. Full-text search with PostgreSQL: tsvector, tsquery, and GIN indexes from Go
11. Redis with go-redis: string, hash, list, set, sorted set operations, Lua scripting, and pub/sub
12. Caching patterns: cache-aside, write-through, and TTL-based invalidation in Go services
13. Distributed locking with Redis: implementing the Redlock algorithm for cross-instance coordination
14. MongoDB with the official Go driver: connecting, CRUD operations, aggregation pipelines, and change streams
15. Database observability: query logging, slow query detection, and connection pool metrics

Week 3 — Phase 4: gRPC & Protocol Buffers

gRPC is the standard for inter-service communication in microservice architectures — and Go is its natural home. This phase covers Protocol Buffers, all four gRPC communication patterns, and building a real gRPC service with a REST gateway.

1. Protocol Buffers (protobuf): defining messages and services in .proto files, scalar types, nested messages, enums, and oneofs
2. protoc and buf: compiling .proto files to Go code — the buf CLI as the modern alternative to raw protoc
3. Unary RPC: the request-response pattern — implementing server and client in Go
4. Server-streaming RPC: the server sends a stream of responses — real-time feed and bulk data export patterns
5. Client-streaming RPC: the client sends a stream of requests — file upload and batch ingestion patterns
6. Bidirectional streaming RPC: full-duplex communication — chat, live collaboration, and sensor telemetry patterns
7. gRPC interceptors: the middleware equivalent for gRPC — logging, authentication, rate limiting, and tracing
8. gRPC authentication: token-based auth via metadata, mTLS for service-to-service authentication
9. gRPC error handling: status codes, error details, and mapping errors to meaningful gRPC status
10. gRPC-Gateway: generating a REST/JSON reverse proxy from your .proto definitions — one codebase, both REST and gRPC
11. gRPC reflection: enabling dynamic service discovery for tools like grpcurl and Postman
12. Deadlines and cancellation: propagating context timeouts across gRPC calls between services
13. gRPC health checking: the standard health check protocol for Kubernetes readiness probes
14. Connect-go: the modern, HTTP/1.1-compatible alternative to traditional gRPC for browser-friendly APIs
15. Testing gRPC services: bufconn for in-memory connection testing without a real network

Week 3–4 — Phase 5: Next.js 15 Frontend

The frontend layer connects to the Go API via REST (or gRPC-Gateway) with full TypeScript type safety, generated directly from the OpenAPI or protobuf definitions.

1. Next.js 15 App Router: server components, client components, layouts, parallel routes, and intercepting routes
2. TypeScript API clients: generating typed clients from the Swagger spec (openapi-typescript) or proto definitions
3. TanStack Query: data fetching, caching, background refetch, optimistic updates, and infinite scroll
4. Authentication: NextAuth.js v5 integrating with the Go JWT backend — session management and protected routes
5. Real-time features: consuming Go server-sent events and WebSocket streams from Next.js
6. Form handling: React Hook Form with Zod validation, mirroring the validation rules in the Go backend
7. Tailwind CSS v4: utility-first styling, design system tokens, and dark mode
8. Data tables and dashboards: TanStack Table for server-side sorted, filtered, and paginated data from Go
9. File uploads: drag-and-drop UI with progress tracking, connecting to the Go multipart upload endpoint
10. Error handling and loading states: error boundaries, Suspense, and skeleton loaders
11. Next.js deployment: Vercel for the frontend — environment variables and preview deployments

Week 4–5 — Phase 6: Microservices, Event-Driven Architecture & Production Readiness

Go was built for distributed systems. This phase covers the patterns and tools for building multi-service architectures — message queues, service discovery, distributed tracing, and the observability stack that makes them operable.

Microservices patterns:

1. Microservice decomposition: when to split a Go monolith, bounded contexts, and the strangler fig pattern
2. Service-to-service communication: synchronous (gRPC) vs asynchronous (message queue) — when each applies
3. API Gateway pattern: a single Go service acting as the entry point — routing, auth, rate limiting, and request transformation
4. Circuit breaker pattern: preventing cascade failures with gobreaker and hystrix-go
5. Retry and exponential backoff: implementing resilient HTTP and gRPC clients
6. Saga pattern: managing distributed transactions across services without two-phase commit

Event-driven architecture:

1. Apache Kafka with Go: producer and consumer patterns using the franz-go or confluent-kafka-go client
2. Consumer groups, partition assignment, and offset management in Kafka
3. NATS and NATS JetStream: the lightweight, cloud-native messaging system — pub/sub, request/reply, and persistent streams
4. RabbitMQ with Go: exchanges, queues, bindings, acknowledgements, and dead-letter queues
5. Outbox pattern: reliably publishing events from a Go service without dual-write inconsistency
6. Event sourcing basics: storing state as a sequence of events — implementation patterns in Go with PostgreSQL

Observability:

1. Structured logging with slog (Go 1.21 standard library logger): JSON output, log levels, and context-attached fields
2. Prometheus metrics in Go: the promhttp handler, Counter, Gauge, Histogram, and Summary metric types
3. Custom business metrics: tracking request rates, queue depths, and business KPIs as Prometheus metrics
4. Distributed tracing with OpenTelemetry Go SDK: spans, trace context propagation across HTTP and gRPC, and exporting to Jaeger or Tempo
5. Grafana dashboards: visualising Go service metrics — latency histograms, error rates, and goroutine counts
6. pprof in production: exposing the Go profiler endpoint for on-demand CPU and memory profiling
7. Go runtime metrics: monitoring GC pause times, goroutine count, and memory allocations in production

Performance and production hardening:

1. Benchmarking Go services: wrk, hey, and k6 for load testing — interpreting latency percentiles
2. Memory allocation optimisation: reducing GC pressure with sync.Pool, pre-allocated buffers, and avoiding unnecessary interface boxing
3. HTTP/2 and HTTP/3: configuring Go's net/http server for modern transport protocols
4. Docker for Go: multi-stage builds producing minimal static binaries in distroless or scratch images
5. Health check and readiness endpoints: standard patterns for Kubernetes probes
6. Rate limiting: token bucket implementation with golang.org/x/time/rate — per-IP and per-user limits
7. Security: input sanitisation, SQL injection prevention, CORS configuration, and security headers

---

🔌 Optional Add-On Modules

(2 weeks each — additional fee per module. Can be taken individually or combined.)

---

Add-On 1: AWS Deployment Track

Deploy Go services across the AWS ecosystem — from Lambda functions that cold-start in milliseconds to high-throughput ECS clusters.

1. AWS Lambda with Go: the aws-lambda-go runtime, handler patterns, cold start performance, and the Lambda Web Adapter for running Gin/Fiber on Lambda
2. AWS API Gateway: HTTP API and REST API configurations, Lambda proxy integration, and custom authorisers in Go
3. ECS Fargate: containerised Go service deployment, task definitions, service auto-scaling, and rolling updates
4. AWS RDS (PostgreSQL): managed database, connection pooling strategies — RDS Proxy for Lambda, direct pools for ECS
5. ElastiCache (Redis): managed Redis clusters for Go service caching and session storage
6. Amazon MSK (Kafka): managed Kafka for Go event-driven architectures
7. AWS S3 with the Go SDK: file storage, presigned URLs, multipart uploads, and S3 event triggers
8. AWS SQS and SNS: decoupled messaging patterns in Go — worker pools consuming SQS queues
9. AWS Secrets Manager: fetching credentials at runtime with the Go SDK
10. Infrastructure as Code with AWS CDK (Go): provisioning the entire stack using Go as the IaC language — CDK Go bindings
11. CloudWatch: structured log ingestion, custom metrics from Go services, dashboards, and alarms
12. CI/CD with GitHub Actions: automated Go build, test, Docker push to ECR, and ECS/Lambda deployment

Add-On 2: Google Cloud Deployment Track

Google Cloud has deep Go roots — Kubernetes and many GCP services are written in Go. This add-on covers deploying Go services on GCP with a focus on Cloud Run and GKE.

1. Cloud Run with Go: serverless container deployment — Go's fast startup and minimal binary size make it ideal for Cloud Run's scale-to-zero model
2. Cloud Run Jobs: running one-off and scheduled Go data processing jobs
3. GKE Autopilot: deploying Go microservices on managed Kubernetes — Deployments, Services, Ingress, and HPA
4. Cloud SQL (PostgreSQL): managed database with Cloud SQL Auth Proxy for secure connections
5. Cloud Pub/Sub with Go: the official Go client for GCP's message queuing service — publishers, subscribers, and push subscriptions
6. Cloud Storage: object storage from Go using the google-cloud-storage client library
7. Secret Manager: fetching secrets in Go services on GCP
8. Cloud Tasks: asynchronous task queuing and scheduling from Go services
9. Firebase with Go: Firestore real-time database and Firebase Auth from Go backend services
10. Terraform on GCP: provisioning Cloud Run, GKE, Cloud SQL, and Pub/Sub with reusable modules
11. Cloud Build: CI/CD for Go — build, test, push to Artifact Registry, and deploy to Cloud Run
12. Cloud Monitoring: structured log ingestion from Go, custom metrics with the Go monitoring client, and uptime checks

Add-On 3: Azure Deployment Track

Deploy Go services on the Microsoft Azure platform — relevant for engineers building for enterprise clients in the Microsoft ecosystem.

1. Azure Container Apps: serverless container hosting for Go services — the Azure equivalent of Cloud Run
2. Azure Kubernetes Service (AKS): deploying Go microservices on managed Kubernetes
3. Azure Container Registry (ACR): building and storing Go Docker images
4. Azure Database for PostgreSQL: managed PostgreSQL with flexible server configuration
5. Azure Cache for Redis: managed Redis for Go caching and session storage
6. Azure Service Bus: message queuing with Go — queues, topics, subscriptions, and dead-letter handling
7. Azure Event Hubs with Go: high-throughput event streaming — the Azure equivalent of Kafka, with the go-eventhubs SDK
8. Azure Blob Storage: file storage from Go using the azure-sdk-for-go
9. Azure Key Vault: secrets and certificate management for Go services
10. Azure API Management: API gateway for Go services — rate limiting, transformation, and developer portal
11. Infrastructure with Terraform on Azure: provisioning Container Apps, AKS, databases, and networking
12. Azure DevOps Pipelines: CI/CD for Go — build, test, containerise, and deploy automation
13. Azure Monitor and Application Insights: distributed tracing and log aggregation for Go services

Add-On 4: Kubernetes & Cloud-Native Go Track

Go is the language Kubernetes is written in — and it is the language used to extend it. This add-on covers operating Go services on Kubernetes at a production level, and building the Kubernetes tooling that platform and infrastructure teams need.

1. Kubernetes production operations for Go services: resource requests/limits, HPA, PDB, and rolling update strategies
2. Helm charts for Go services: writing reusable, configurable charts for your application
3. The controller-runtime library: the foundation for building Kubernetes controllers and operators in Go
4. Custom Resource Definitions (CRDs): extending the Kubernetes API with your own resource types
5. Writing a Kubernetes Operator in Go with kubebuilder: reconcile loops, status conditions, and finalizers
6. client-go: the official Kubernetes Go client — listing, watching, and patching resources programmatically
7. Admission webhooks: validating and mutating Kubernetes resources before they are persisted — written in Go with Gin
8. Kubernetes scheduling: custom schedulers and scheduler extenders in Go
9. Service mesh with Istio: traffic management, mTLS, and observability for Go microservice meshes
10. Telepresence: developing and debugging Go services that run in a Kubernetes cluster from your local machine
11. ArgoCD and GitOps: deploying Go service Helm charts via GitOps workflows

Add-On 5: AI Integration Track

Go is increasingly used as the high-performance serving layer for AI features — calling LLM APIs, orchestrating inference pipelines, and handling the volume of concurrent requests that Python frameworks struggle with.

1. Calling LLM APIs from Go: OpenAI, Anthropic, and Google Gemini using sashabaranov/go-openai and custom reqwest clients
2. Streaming LLM responses from Go to the Next.js frontend via Server-Sent Events
3. Structured JSON output from LLMs: parsing responses into typed Go structs with encoding/json
4. Embeddings from Go: generating text embeddings via OpenAI API and storing in PostgreSQL with pgvector
5. Building a RAG pipeline in Go: document ingestion, chunking, embedding generation, pgvector storage, and retrieval — all in concurrent Go
6. Qdrant Go client: querying a dedicated vector database with metadata filtering from Go
7. LLM prompt management: template-based prompt construction with Go's text/template and html/template
8. Concurrent LLM request processing: using goroutines and worker pools to process thousands of LLM requests efficiently
9. Semantic caching with Redis: storing embeddings of previous queries and returning cached responses for semantically similar inputs
10. AI feature rate limiting and cost tracking: per-user token budgets and spend monitoring in Go
11. ONNX Runtime with Go: running exported ML models directly in Go services for zero-latency inference without Python
12. Whisper API integration: speech-to-text transcription in Go-powered media processing pipelines

Add-On 6: High-Performance Systems & FinTech Track

Go's concurrency model, low GC overhead, and single-binary deployment make it a natural choice for financial systems, trading infrastructure, and high-throughput data processing pipelines. This add-on covers the performance engineering and domain patterns that FinTech and systems engineering roles require.

Week 1 — Performance Engineering & High-Throughput Systems:

1. Go memory model in depth: happens-before relationships, atomic operations, and writing correct concurrent code
2. Zero-allocation programming: avoiding heap allocations in the hot path with sync.Pool, stack allocation, and pre-allocated slices
3. CPU cache efficiency: struct field ordering, avoiding false sharing, and cache-line-aware data structures
4. SIMD and assembly: calling hand-written assembly from Go for vectorised numerical operations
5. High-performance serialisation: protobuf vs JSON vs MessagePack vs FlatBuffers benchmarks — choosing the right wire format
6. eBPF with Go: using Cilium's ebpf-go for kernel-level network observability and packet filtering
7. High-performance networking: raw TCP servers with net package, custom protocol implementations, and connection multiplexing
8. WebSocket at scale: handling 100,000+ concurrent WebSocket connections in a single Go process
9. Profiling and optimisation workflow: pprof CPU profiles, memory profiles, mutex contention profiles, and flamegraphs — systematic hotspot identification and elimination

Week 2 — FinTech Domain Patterns:

1. Financial arithmetic in Go: why float64 is wrong for money, fixed-point arithmetic, and the shopspring/decimal library
2. Double-entry accounting engine: implementing a ledger in Go with PostgreSQL — accounts, journal entries, and balance queries
3. Payment processing: integrating Stripe, HBL, and local Pakistani payment gateways from Go
4. Transaction idempotency: ensuring payments are processed exactly once using idempotency keys and PostgreSQL advisory locks
5. WebSocket market data feeds in Go: connecting to exchange WebSocket APIs (Binance, Bybit) — parsing order book updates and trade streams with minimal allocation
6. REST trading APIs: placing and cancelling orders on CEX exchanges from Go — HMAC-SHA256 request signing
7. Order book implementation in Go: maintaining a sorted bid/ask book using a custom skip-list or sorted slice with binary search
8. Portfolio and PnL tracking: real-time unrealised and realised PnL calculation across multiple positions
9. Risk management layer: position limits, exposure checks, and kill-switch logic as Go middleware
10. Backtesting framework: replaying historical OHLCV data through a strategy and computing performance metrics
11. Regulatory compliance patterns: audit trails, immutable event logs, and data retention policies in Go + PostgreSQL
12. KYC/AML API integration: connecting to identity verification providers (Jumio, Onfido) from Go

📅 Schedule & Timings

Choose one group only based on your availability. Max 5 candidates per group to ensure individual attention and hands-on support.

Weekday Groups:

• Group 1: Mon–Wed, 10 AM – 1 PM
• Group 2: Mon–Wed, 4 PM – 7 PM

Weekend Groups:

• Group 3: Sat & Sun, 10 AM – 2 PM
• Group 4: Sat & Sun, 4 PM – 8 PM

📍 Location: In-house training in Islamabad
📱 Online option may be arranged for out-of-city participants

🛠️ Core Tools & Technologies

• Language: Go 1.22+, go mod, golangci-lint, govulncheck
• Web Frameworks: Gin, Fiber, net/http standard library
• gRPC: protoc, buf CLI, grpc-go, gRPC-Gateway, Connect-go
• Database: GORM, sqlx, sqlc, golang-migrate, pgvector, go-redis, MongoDB Go driver
• Messaging: franz-go (Kafka), NATS, go-amqp (RabbitMQ)
• Auth: golang-jwt/jwt, bcrypt, Casbin
• Config: Viper, envconfig
• Observability: slog, Prometheus Go client, OpenTelemetry Go SDK, pprof
• Testing: testify, testcontainers-go, httptest, gomock
• DevOps: Docker (multi-stage, distroless), GitHub Actions
• Frontend: Next.js 15, TypeScript, Tailwind CSS v4, TanStack Query, NextAuth.js v5

✅ Prerequisites

• Comfortable with at least one backend language (Python, Node.js, PHP, Java, or similar)
• Familiar with REST API concepts, HTTP, and JSON
• Basic SQL and relational database knowledge
• No prior Go experience required

🎯 Who This Is For

• Backend engineers who want to move from Python or Node.js to a higher-performance stack
• Engineers targeting cloud infrastructure, DevOps tooling, or platform engineering roles
• Developers building high-concurrency services: APIs, real-time systems, or data pipelines
• Engineers pursuing remote roles where Go experience commands a salary premium
• FinTech or trading systems developers looking for a language built for their domain

💳 Course Fee & Booking

• ⌛ Core Program Duration: 5 Weeks
• ➕ Each Add-On Module: 2 additional weeks (additional fee per module)
• 📦 Available Add-Ons: AWS · Google Cloud · Azure · Kubernetes & Cloud-Native · AI Integration · High-Performance Systems & FinTech
• 🔒 Seats: 5 only per group

👉 Click here to book via WhatsApp