Description

Introduction

The “Getting Started with q Language Programming” training is designed to provide a strong foundation in the q programming language used in kdb+, the high-performance, columnar, time-series database widely adopted in financial services, telecom, energy, and real-time analytics.

This course introduces participants to q’s expressive syntax, vector-oriented operations, and functional programming paradigm. Learners will gain hands-on experience in data manipulation, querying, and scripting using q, enabling them to work confidently with high-frequency and large-scale datasets.

By the end of the training, participants will understand how q works internally, how to write efficient queries, and how to build basic analytical solutions using kdb+.

Prerequisites

1. Basic understanding of programming concepts (variables, functions, loops, conditionals)
2. Familiarity with databases and SQL fundamentals
3. Basic knowledge of data structures (arrays, lists, dictionaries)
4. Understanding of data analytics concepts is a plus
5. No prior experience in q is required

Table of Contents

Module 1: Introduction to kdb+ and q

1. Overview of kdb+ architecture
2. What is q language?
3. kdb+ vs traditional RDBMS
4. Installation and setup
5. q console basics

Module 2: q Fundamentals

1. Data types in q (atoms, lists)
2. Basic operators and expressions
3. Variables and assignments
4. Input/output operations
5. Comments and script execution

Module 3: Working with Lists and Dictionaries

1. Creating and manipulating lists
2. Nested lists
3. Dictionaries and keyed dictionaries
4. Enumerations
5. Symbol data type

Module 4: Tables in q

1. Creating tables
2. Keyed tables
3. In-memory vs on-disk tables
4. Table attributes
5. Meta data and schema inspection

Module 5: Querying Data in q

1. Select statements
2. Where clauses
3. Aggregations and grouping
4. Update and delete operations
5. Functional query form

Module 6: Functions and Functional Programming

1. Defining functions in q
2. Lambda expressions
3. Adverbs (each, over, scan)
4. Iterators and projections
5. Function composition

Module 7: Date, Time & Temporal Data

1. Date, time, datetime, timestamp types
2. Temporal arithmetic
3. Time-series queries
4. Windowed aggregations

Module 8: Scripting and Execution

1. Writing q scripts
2. Loading and saving data
3. Command-line execution
4. Environment variables
5. Basic performance considerations

Module 9: Error Handling & Debugging

1. Common error types
2. Debugging techniques
3. Best practices for writing clean q code

Module 10: Hands-On Labs & Mini Project

1. Building a small time-series analytics solution
2. Query optimization exercises
3. Real-world dataset practice
4. Final assessment