Description

Introduction

Data Modeling & Partitioning Strategies in kdb+ is a comprehensive, hands-on training designed to help professionals architect high-performance time-series databases using kdb+ and the q language.

This course focuses on designing scalable schemas, implementing optimal partitioning strategies, and building high-throughput, low-latency data architectures for financial markets, IoT, telecom, and real-time analytics environments. Participants will learn how to structure historical and real-time data, balance memory and disk usage, and optimize query performance for enterprise-grade deployments.

By the end of the training, learners will be able to design robust kdb+ database architectures that support billions of records while maintaining millisecond-level performance.

Prerequisites

1. Basic understanding of kdb+ architecture
2. Working knowledge of q language fundamentals
3. Familiarity with time-series data concepts
4. Basic understanding of database design principles
5. Experience with Linux/Unix command line (recommended)
6. Prior exposure to HDB/RDB concepts (preferred but not mandatory)

Table of Contents

Module 1: Foundations of Data Modeling in kdb+

1. Overview of kdb+ database architecture (RDB, HDB, IDB)
2. Column-oriented storage fundamentals
3. Time-series data characteristics
4. kdb+ file structure and splayed tables
5. In-memory vs on-disk tables

Module 2: Schema Design Best Practices

1. Designing efficient table schemas
2. Choosing appropriate data types
3. Symbol handling and enumeration strategies
4. Managing high-cardinality columns
5. Normalization vs denormalization in kdb+
6. Designing for compression and storage efficiency

Module 3: Partitioning Strategies in kdb+

1. Why partitioning matters
2. Date-based partitioning (daily/monthly/yearly)
3. Intra-day partitioning techniques
4. Segmented vs flat partition structures
5. Custom partitioning strategies
6. Partition directory structure and management

Module 4: Historical Database (HDB) Design

1. Building a production-grade HDB
2. Creating and maintaining partitioned databases
3. End-of-day processing workflows
4. Loading and querying partitioned data
5. Managing large-scale historical datasets

Module 5: Real-Time Database (RDB) & Integration

1. Designing RDB schemas
2. Efficient intraday storage
3. RDB to HDB rollover processes
4. Managing write performance
5. Handling tick data efficiently

Module 6: Performance Optimization Techniques

1. Attribute usage (sorted, grouped, parted)
2. Indexing strategies
3. Query optimization patterns
4. Avoiding common performance bottlenecks
5. Memory management best practices

Module 7: Scalability & Enterprise Architecture

1. Distributed kdb+ architecture
2. Multi-process data handling
3. Sharding strategies
4. Load balancing and failover design
5. High-availability considerations

Module 8: Data Maintenance & Governance

1. Data validation strategies
2. Managing schema evolution
3. Archiving strategies
4. Backup and recovery planning
5. Monitoring database health

Module 9: Hands-On Labs & Case Studies

1. Designing a market data schema
2. Building a partitioned HDB from scratch
3. Optimizing slow queries
4. Scaling to billions of records
5. Real-world financial data modeling case study