Description

Introduction

This advanced training program is designed for system architects, DevOps engineers, and data platform administrators responsible for deploying and managing enterprise-scale kdb+ environments.

Participants will gain hands-on expertise in architecting, deploying, scaling, securing, and monitoring distributed kdb+ clusters across on-premises and cloud infrastructures. The course focuses on high availability, fault tolerance, performance optimization, orchestration, and operational best practices aligned with enterprise production standards.

By the end of this training, learners will be able to design and operate mission-critical kdb+ infrastructures that support real-time analytics, high-frequency data ingestion, and large-scale time-series workloads.

Prerequisites

1. Strong working knowledge of kdb+ and the q language
2. Understanding of Linux/Unix system administration
3. Basic networking knowledge (TCP/IP, ports, firewalls)
4. Familiarity with distributed systems concepts
5. Exposure to cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform (recommended)
6. Basic scripting knowledge (Bash/Python is beneficial)

Table of Contents

Module 1: Enterprise kdb+ Architecture Fundamentals

1. Single-node vs multi-node architecture
2. kdb+ process types (Tickerplant, RDB, HDB, Gateway)
3. Data flow in real-time environments
4. Designing for scale and resilience

Module 2: Installation & Environment Setup

1. Enterprise installation strategies
2. Licensing considerations
3. OS-level performance tuning
4. Filesystem optimization (SSD, RAID, NVMe)
5. Containerized deployment basics

Module 3: Distributed Cluster Design

1. Multi-tier kdb+ architecture
2. Segregating real-time and historical workloads
3. Horizontal vs vertical scaling
4. Partitioning strategies
5. Multi-region deployment design

Module 4: High Availability & Fault Tolerance

1. Redundant tickerplants
2. Failover mechanisms
3. Replication strategies
4. Backup and disaster recovery planning
5. Active-passive vs active-active setups

Module 5: Performance Optimization at Scale

1. Memory management techniques
2. IPC optimization
3. Query performance tuning
4. Compression strategies
5. Parallel processing in q

Module 6: Data Management & Storage Strategy

1. HDB design best practices
2. Partitioning and segmentation
3. Archiving strategies
4. Data lifecycle management
5. Cold vs hot storage tiers

Module 7: Security & Access Control

1. User authentication mechanisms
2. Role-based access control (RBAC)
3. TLS/SSL configuration
4. Secure IPC connections
5. Audit logging and compliance

Module 8: Monitoring & Observability

1. Health checks and heartbeat monitoring
2. Resource monitoring (CPU, memory, I/O)
3. Log aggregation strategies
4. Alerting frameworks
5. Integrating with enterprise monitoring tools

Module 9: Cloud Deployment & Orchestration

1. Deploying kdb+ on Amazon Web Services
2. Deploying kdb+ on Microsoft Azure
3. Infrastructure as Code concepts
4. Container orchestration fundamentals
5. Auto-scaling strategies

Module 10: DevOps & CI/CD for kdb+

1. Version control strategies
2. Automated deployment pipelines
3. Environment promotion (Dev → QA → Prod)
4. Blue-green deployments
5. Configuration management

Module 11: Troubleshooting & Production Support

1. Debugging distributed systems
2. Performance bottleneck identification
3. Memory leak diagnosis
4. Recovery from process crashes
5. Incident response best practices

Module 12: Enterprise Case Studies & Hands-on Labs

1. Designing a production-grade kdb+ cluster
2. Simulating failover scenarios
3. Deploying multi-node architecture
4. Performance benchmarking exercise
5. Real-world architecture review