Description

Introduction

Financial markets generate massive volumes of high-frequency, time-series data every second — from tick-by-tick trades and quotes to order book updates and derived analytics. Building a scalable, low-latency market data platform requires high-performance data ingestion, compression, querying, and real-time analytics.

This training on Designing Scalable Market Data Systems Using kdb+ provides a comprehensive, hands-on approach to architecting, implementing, and optimizing enterprise-grade market data systems using kdb+ and the q language.

Participants will learn how to design tick architectures, implement real-time streaming pipelines, build historical data stores, manage partitioning strategies, and optimize performance for ultra-low latency trading and analytics environments.

This course is ideal for financial technologists, data engineers, quantitative developers, and solution architects working in capital markets.

Prerequisites

1. Basic understanding of financial markets (equities, FX, derivatives, order books)
2. Familiarity with time-series data concepts
3. Basic programming knowledge (preferably in Python, Java, or C++)
4. Exposure to Linux/Unix environments
5. Prior exposure to q is helpful but not mandatory

Table of Contents

Module 1: Market Data Fundamentals

1. Types of market data (Trades, Quotes, Level 2, Reference Data)
2. Tick data characteristics and challenges
3. Real-time vs Historical data systems
4. Latency, throughput, and scalability requirements
5. Market data vendor feeds overview

Module 2: kdb+ Architecture Deep Dive

1. Core architecture of kdb+
2. In-memory vs on-disk databases
3. Columnar storage and compression mechanisms
4. HDB (Historical Database) vs RDB (Real-Time Database)
5. Tickerplant architecture
6. Process communication in kdb+

Module 3: Designing a Scalable Tick Architecture

1. Tickerplant design patterns
2. Feed handlers and data normalization
3. Real-time data capture strategies
4. Write-ahead logging
5. Failover and redundancy mechanisms
6. High-availability deployment models

Module 4: Data Modeling for Market Data

1. Time-series schema design
2. Sym, timestamp, and partitioning strategies
3. Splayed vs partitioned tables
4. Segmented historical databases
5. Reference and sym file management
6. Data compression best practices

Module 5: Real-Time Processing & Analytics

1. Real-time aggregation techniques
2. VWAP, TWAP, OHLC calculations
3. Intraday analytics
4. Streaming queries and subscriptions
5. Event-driven processing in q
6. Managing intraday state efficiently

Module 6: Historical Data Management

1. Building and maintaining HDB
2. End-of-day (EOD) processing
3. Data compaction and archival strategies
4. Data validation and reconciliation
5. Backfilling and correction workflows

Module 7: Performance Optimization

1. Query profiling and benchmarking
2. Memory management techniques
3. CPU optimization strategies
4. Parallel processing in q
5. Scaling across cores and servers
6. Latency measurement and tuning

Module 8: Distributed & Enterprise Deployment

1. Multi-node kdb+ architectures
2. Load balancing strategies
3. Gateway processes
4. Entitlements and access control
5. Containerization and cloud deployment
6. Monitoring and observability

Module 9: Integration & Ecosystem

1. Integration with Python and Java
2. REST APIs and external connectivity
3. Streaming integrations (Kafka concepts)
4. Interfacing with trading systems
5. Reporting and visualization tools

Module 10: Production Best Practices

1. Disaster recovery planning
2. Backup and restore strategies
3. Security considerations
4. Governance and compliance requirements
5. Operational runbooks
6. Common anti-patterns in market data systems