15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Big data engineers
- Cloud engineers
- DevOps engineers
- Platform engineers
- Distributed systems developers
- Data architects
- Solutions architects
- Enterprise architects
- API developers
- Microservices developers
- Technology and IT managers
- Professionals responsible for developing scalable, high-performance data systems
Session Objectives
- Understand GoLang’s architecture and performance benefits
- Develop concurrent and parallel data processing programs
- Build resilient and scalable APIs and microservices
- Interface Go applications with relational and NoSQL databases
- Implement high-throughput event-driven data systems
- Use Go routines, channels, and worker pools effectively
- Secure Go services and handle error management
- Write unit tests and benchmark Go code
- Integrate Go with data message brokers like Kafka or NATS
- Deploy Go services using containers and CI/CD pipelines
- Optimize Go-based systems for performance and memory usage
About the Course
This advanced training equips participants with the practical skills required to design, develop, optimize, and deploy scalable data-intensive systems using Go (Golang). The course focuses on Go's strengths in high-performance computing, concurrency, distributed systems, data processing, APIs, microservices, and cloud-native application development.Participants will learn how to build efficient data services capable of handling high throughput, concurrent workloads, large datasets, and real-time processing requirements. The training covers Go programming fundamentals, concurrency patterns, data structures, database integration, distributed architectures, streaming, APIs, testing, observability, performance optimization, and production deployment.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Go's design philosophy and compiler architecture
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Workshop 1.2:
Setting up the Go environment and workspace
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Workshop 1.3:
Introduction to Go modules and packages
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Workshop 1.4:
Project structure and dependency management
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Workshop 1.5:
Running and debugging basic Go programs
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Workshop 2.1:
Variables, types, and constants
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Workshop 2.2:
Control structures and looping mechanisms
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Workshop 2.3:
Functions, parameters, and return values
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Workshop 2.4:
Error handling and panic recovery
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Workshop 2.5:
Code formatting and documentation
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Workshop 3.1:
Understanding structs and embedded types
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Workshop 3.2:
Defining and implementing interfaces
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Workshop 3.3:
Interface polymorphism in Go
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Workshop 3.4:
Value vs pointer receivers
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Workshop 3.5:
Organizing reusable components
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Workshop 4.1:
Introduction to Go’s concurrency model
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Workshop 4.2:
Creating and managing goroutines
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Workshop 4.3:
Channel types: unbuffered and buffered
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Workshop 4.4:
Synchronization patterns and worker pools
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Workshop 4.5:
Avoiding deadlocks and race conditions
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Workshop 5.1:
Reading and writing files efficiently
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Workshop 5.2:
Parsing and encoding JSON data
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Workshop 5.3:
Struct tags and data binding
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Workshop 5.4:
File streaming and data chunking
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Workshop 5.5:
Logging and error management
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Workshop 6.1:
Connecting to PostgreSQL/MySQL with database/sql
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Workshop 6.2:
Executing queries and scanning results
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Workshop 6.3:
Connection pooling and transaction management
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Workshop 6.4:
ORM libraries (GORM, sqlc)
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Workshop 6.5:
Handling database errors gracefully
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Workshop 7.1:
Building APIs using net/http and Gorilla Mux
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Workshop 7.2:
Handling routes, middleware, and JSON responses
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Workshop 7.3:
Authentication and rate limiting
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Workshop 7.4:
Testing HTTP endpoints
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Workshop 7.5:
Creating secure and versioned APIs
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Workshop 8.1:
Microservices architecture fundamentals
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Workshop 8.2:
Service discovery and inter-service communication
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Workshop 8.3:
Resilience with retries and circuit breakers
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Workshop 8.4:
Logging, tracing, and observability
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Workshop 8.5:
Managing configuration and environment variables
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Workshop 9.1:
Integrating Go with Kafka or NATS
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Workshop 9.2:
Publishing and consuming streaming data
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Workshop 9.3:
Message serialization formats (Avro, Protobuf)
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Workshop 9.4:
Batch vs stream processing architecture
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Workshop 9.5:
Scaling Go consumers
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Workshop 10.1:
Benchmarking with Go’s testing tools
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Workshop 10.2:
Profiling CPU, memory, and blocking calls
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Workshop 10.3:
Garbage collection tuning and memory leaks
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Workshop 10.4:
Efficient string and slice operations
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Workshop 10.5:
Avoiding common performance pitfalls
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Workshop 11.1:
Writing unit tests and using test suites
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Workshop 11.2:
Test coverage and mocking dependencies
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Workshop 11.3:
Table-driven testing pattern
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Workshop 11.4:
Debugging with Delve and VS Code
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Workshop 11.5:
Continuous testing with CI pipelines
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Workshop 12.1:
Integrating with Redis, MongoDB, or DynamoDB
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Workshop 12.2:
CRUD operations and indexing strategies
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Workshop 12.3:
Cloud storage SDKs for S3, GCS, Azure Blob
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Workshop 12.4:
Writing cloud-agnostic storage handlers
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Workshop 12.5:
Managing data backup and restore
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Workshop 13.1:
Parsing flags and command-line arguments
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Workshop 13.2:
Reading large input streams
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Workshop 13.3:
Batch processing using pipelines
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Workshop 13.4:
Formatting outputs and exporting results
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Workshop 13.5:
Scheduling cron jobs and automation
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Workshop 14.1:
Writing Dockerfiles for Go services
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Workshop 14.2:
Multi-stage builds and image optimization
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Workshop 14.3:
Running Go in Kubernetes
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Workshop 14.4:
Deploying Go apps on cloud platforms
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Workshop 14.5:
Environment-based configuration management
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Workshop 15.1:
Designing a real-world high-throughput data service
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Workshop 15.2:
Building API, streaming layer, and persistence
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Workshop 15.3:
Implementing authentication and validation
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Workshop 15.4:
Load testing and performance tuning
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Workshop 15.5:
Deploying the complete solution with monitori
