Description

Introduction

In high-frequency and real-time data environments, the ability to ingest, process, and analyze streaming data with ultra-low latency is critical. Streaming & Tick Architecture in kdb+ is designed to provide a deep, hands-on understanding of real-time data pipelines using kdb+ and the q language.

This training explores the core concepts of tick architecture — including tickerplants, real-time databases (RDB), historical databases (HDB), and logging mechanisms — enabling participants to build scalable, fault-tolerant, and high-performance streaming systems.

Participants will gain practical expertise in designing, implementing, and optimizing streaming pipelines suitable for financial markets, IoT, telecom, and other time-series intensive industries.

Prerequisites

1. Basic understanding of kdb+ & q fundamentals
2. Knowledge of tables, lists, dictionaries, and functions in q
3. Understanding of time-series data concepts
4. Familiarity with Linux command line (preferred)
5. Basic networking concepts (IPC, client-server architecture)
6. kdb+ & q Fundamentals
7. Time-Series Data Modeling in kdb+

Table of Contents

Module 1: Foundations of Streaming in kdb+

1. Real-time vs Batch Processing
2. Overview of Tick Architecture
3. kdb+ Process Roles and Communication
4. IPC Mechanisms in q
5. Publishing & Subscribing Model

Module 2: The Tickerplant (TP)

1. Role of the Tickerplant
2. Schema Design for Streaming Data
3. Writing a Basic Tickerplant
4. Logging & Data Persistence
5. Managing Subscriptions
6. End-of-Day (EOD) Processing

Module 3: Real-Time Database (RDB)

1. RDB Architecture and Responsibilities
2. Subscribing to the Tickerplant
3. In-Memory Data Management
4. Handling Intraday Queries
5. Performance Considerations
6. Restart & Recovery Strategies

Module 4: Historical Database (HDB) Integration

1. Data Lifecycle in Tick Architecture
2. EOD Data Migration (RDB → HDB)
3. Partitioning Strategies
4. Splayed & Partitioned Tables
5. Querying Across RDB and HDB

Module 5: Building a Complete Tick System

1. Process Topology Design
2. Multi-Asset & Multi-Feed Setup
3. Load Balancing Techniques
4. Chained Tickerplants
5. Data Replay & Backfilling

Module 6: Advanced Streaming Patterns

1. Derived & Calculated Streams
2. Real-Time Aggregations
3. Windowed Analytics
4. Alerting & Event Processing
5. Complex Event Processing (CEP) in q

Module 7: Performance Optimization

1. Latency Measurement & Monitoring
2. Memory Optimization Techniques
3. Efficient Table Design
4. Asynchronous vs Synchronous Processing
5. Scaling Strategies (Vertical & Horizontal)

Module 8: Fault Tolerance & Production Deployment

1. High Availability Architectures
2. Replication & Failover Strategies
3. Logging & Auditing
4. Monitoring & Health Checks
5. Deployment Best Practices

Module 9: Hands-On Labs & Capstone

1. Build a Live Market Data Simulation
2. Implement a Production-Ready Tick Architecture
3. Real-Time Trade & Quote Analytics
4. End-to-End Deployment Exercise