Description

Introduction

Time-series data is at the heart of high-frequency trading, financial analytics, IoT telemetry, and real-time monitoring systems. kdb+, powered by the expressive q language, is one of the fastest and most efficient time-series databases used in capital markets and data-intensive industries.

This training provides a comprehensive, hands-on understanding of how to ingest, store, query, and analyze large-scale time-series data using kdb+. Participants will learn best practices for designing time-series schemas, optimizing performance, managing historical and real-time data, and implementing efficient analytics workflows.

By the end of this course, learners will confidently build high-performance time-series solutions using kdb+ for real-world applications.

Prerequisites

1. Basic understanding of databases and data structures
2. Familiarity with command-line environments
3. Basic programming knowledge
4. Prior exposure to kdb+ & q
5. Understanding of time-series concepts (timestamps, intervals, sampling) is beneficial

Table of Contents

Module 1: Introduction to Time-Series in kdb+

1. What is Time-Series Data?
2. Why kdb+ for Time-Series?
3. Overview of kdb+ Architecture (RDB, HDB, IDB)
4. Real-time vs Historical Data Handling

Module 2: Data Types & Temporal Structures

1. Temporal Data Types in q (date, time, timestamp, timespan)
2. Working with Nanosecond Precision
3. Type Casting & Conversions
4. Handling Time Zones

Module 3: Time-Series Table Design

1. In-Memory vs On-Disk Tables
2. Partitioned Databases
3. Columnar Storage Model
4. Primary Keys & Attributes (s, p, g)
5. Schema Design Best Practices

Module 4: Data Ingestion Techniques

1. Loading CSV and External Data Sources
2. Real-Time Data Feeds
3. Batch Processing
4. Using .u.upd for Real-Time Updates
5. Data Validation & Cleaning

Module 5: Querying Time-Series Data

1. Basic Select Queries
2. Filtering by Time Ranges
3. Where Clauses & Conditional Queries
4. Using within, between
5. Performance-Optimized Queries

Module 6: Time-Based Aggregations

1. Grouping by Time Buckets
2. xbar and Time Binning
3. Windowed Aggregations
4. Moving Averages & Rolling Calculations
5. VWAP & Financial Metrics

Module 7: As-Of Joins & Temporal Joins

1. Understanding aj, aj0
2. Window Joins
3. Handling Irregular Time-Series
4. Trade & Quote (TAQ) Analysis

Module 8: Managing Historical Databases (HDB)

1. Creating Partitioned HDB
2. End-of-Day Processing
3. Data Compaction
4. Database Maintenance
5. Querying Across Partitions

Module 9: Performance Optimization

1. Applying Table Attributes
2. Memory Management
3. Query Profiling
4. Scaling Strategies
5. Multi-threading in kdb+

Module 10: Real-World Use Cases

1. High-Frequency Trading Analytics
2. Market Data Analysis
3. IoT Sensor Monitoring
4. Real-Time Dashboard Backend

Module 11: Hands-On Lab

1. Build a Real-Time Time-Series Pipeline
2. Create and Query an HDB
3. Implement As-Of Joins
4. Optimize a Large Dataset Query
5. Mini Project: Financial Tick Data Analysis