Description

Introduction

In modern data-driven enterprises, deploying and maintaining high-performance time-series systems requires more than just strong development skills—it demands robust DevOps practices. This training program focuses on implementing DevOps principles and CI/CD pipelines specifically tailored for kdb+ & q environments.

Participants will learn how to automate builds, testing, deployments, monitoring, and scaling of kdb+ systems across development, staging, and production environments. The course emphasizes version control, containerization, orchestration, automated testing of q scripts, performance validation, and production-grade deployment strategies for low-latency systems.

By the end of this training, learners will be able to design, implement, and manage CI/CD pipelines optimized for kdb+ infrastructures in financial and enterprise environments.

Prerequisites

1. Basic understanding of kdb+ architecture
2. Working knowledge of q programming
3. Familiarity with Linux/Unix command line
4. Basic understanding of Git and version control concepts
5. General awareness of DevOps principles
6. Understanding of time-series database concepts

Table of Contents

Module 1: DevOps Fundamentals for kdb+

1. DevOps principles and lifecycle
2. CI/CD overview in time-series systems
3. Dev vs Production in kdb+ environments
4. Infrastructure challenges in low-latency systems

Module 2: Version Control for q Projects

1. Structuring kdb+ projects for Git
2. Managing schema, scripts, and configuration files
3. Branching strategies (GitFlow, trunk-based)
4. Code review best practices for q
5. Handling large datasets in repositories

Module 3: Automated Testing in q

1. Unit testing frameworks for q
2. Writing testable q functions
3. Mocking data in kdb+
4. Regression testing for time-series systems
5. Performance benchmarking tests

Module 4: CI Pipelines for kdb+

1. CI concepts and workflow
2. Build automation for q projects
3. Static code validation
4. Automated test execution
5. Pipeline triggers and artifacts

Module 5: Containerization of kdb+

1. Introduction to Docker
2. Creating Dockerfiles for kdb+
3. Managing runtime configurations
4. Secure handling of license files
5. Multi-container architectures (Tickerplant, RDB, HDB)

Module 6: Orchestration & Environment Management

1. Kubernetes fundamentals
2. Deploying kdb+ containers
3. Stateful vs stateless components
4. ConfigMaps and Secrets
5. Scaling real-time kdb+ systems

Module 7: Deployment Strategies

1. Blue-Green deployment
2. Canary releases
3. Rolling updates
4. Zero-downtime deployment techniques
5. Rollback strategies

Module 8: Monitoring & Observability

1. Logging strategies in kdb+
2. Metrics collection
3. Performance monitoring
4. Alerting strategies
5. Latency tracking in production

Module 9: Security & Compliance

1. Secure configuration management
2. Access control in kdb+
3. Secrets management
4. Audit logging
5. Regulatory considerations in financial systems

Module 10: Production Hardening & Disaster Recovery

1. Backup strategies for HDB
2. High availability setups
3. Failover mechanisms
4. Data replication approaches
5. Incident response workflows

Module 11: End-to-End Project

1. Designing a CI/CD pipeline for a sample kdb+ architecture
2. Containerizing components
3. Automated testing and validation
4. Production deployment simulation
5. Monitoring and rollback exercise