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KDB-X Guides Overview

This page explains what the Guides section is, why it matters, and introduces the available learning paths and resources.

KDB-X Guides are your starting point for learning how to use KDB-X effectively. They help you build a foundation in the q language, understand how KDB-X operates, and apply these principles to real analytics workflows. This section brings together conceptual overviews, tutorials, and practical examples to support everything from first exposure to q through to building scalable systems.

Whether you’re new to q or looking to deepen your understanding of the language and its ecosystem, this section provides a structured learning path – from first principles to advanced topics – using real examples and hands-on guidance.

Before diving in, here’s a quick look at the key technologies that make up the KDB-X platform:

  • q – an expressive, array-oriented programming language purpose-built for high-performance time-series and vector analytics. Its concise syntax and functional paradigm make it ideal for querying, transforming, and analyzing large, in-memory datasets in real time.
  • KDB-X – KX’s next-generation, modular analytics platform for real-time, AI-driven workloads. Built on the proven performance of kdb+, it unifies time-series, vector, and AI processing in one scalable environment. With native support for q, SQL, and Python, KDB-X enables developers to build and deploy data-intensive applications faster – for both structured and unstructured data – without stitching together separate tools or systems.
  • KDB-X Python – a powerful Python interface to q, bridging traditional data science workflows with KX technology. It allows seamless conversion between q and Python objects, enabling users to query data, run analytics, and integrate AI/ML models using Python libraries – all within one unified environment.

How it fits together

KDB-X Guides helps you progress from language fundamentals to building scalable systems with data and AI:

q language → KDB-X platform → KDB-X Python / AI integrations

Want to dive straight in?

  1. Start with the Brief Introduction to q and KDB-X
  2. Review practical topics in the How-To Guides
  3. Explore KDB-X Python and Modules
  4. Continue with the Q for Mortals Overview

What you’ll learn

By exploring the Guides section, you will:

  • Understand the fundamentals of the q language.
  • Learn how KDB-X extends these technologies into a scalable, cloud-native analytics platform.
  • Use KDB-X Python and AI libraries to extend KDB-X for advanced analytics.
  • Apply your knowledge to real-world scenarios.
  • Explore q’s syntax, data types, and database structures through Q for Mortals.

Learning path

1. Brief introduction to q and KDB-X

Start here for a concise, high-level overview of q and the KDB-X platform. Learn what makes q unique, how it relates to SQL and Python, and where KDB-X fits in modern analytics workflows.

Brief Introduction to q and KDB-X

2. General guidance

Get comfortable working interactively in the KDB-X environment. Learn how to use the q terminal (REPL) effectively and how to work with the embedded line editor to improve productivity when writing and editing q code.

3. Practical how-to guides

The How-To series provides step-by-step examples of how to use q and KDB-X for real-world tasks. You'll find them grouped by topic for easy navigation:

Basics

Core language and workflow concepts for everyday development:

Querying

Data retrieval, sorting, transformation, and analysis using q and qSQL:

I/O and communication

Communication with external processes and systems using IPC, messaging patterns, and network protocols:

Performance Tips

Guidance on efficient q code and optimal use of KDB-X performance features:

Performance Tips

Interact with databases

Database storage, persistence, relationships, and maintenance in KDB-X:

Manage streaming data

Working with code

Tools and techniques for inspecting and troubleshooting q code:

Debug q Code

Support a KDB-X system

Operational guidance for monitoring and maintaining a running KDB-X system:

4. AI libraries

Explore how KDB-X integrates with AI and vector search capabilities. Learn how to build intelligent data systems using similarity search, fuzzy filters, and other ML-powered indexing and retrieval techniques.

5. KDB-X Python

Learn how to connect Python and q using KDB-X Python – KX’s official Python interface for KDB-X. Use KDB-X Python to query, transform, and analyze data in q from Python notebooks and AI workflows.

KDB-X Python Overview

6. Q for Mortals

Your structured path through the q language. Q for Mortals, by Jeff Borror, introduces q step by step – from basic data types and list processing to functions, tables, and queries – then connects those concepts to the kdb+ database engine.

  • Start your journey here: Q for Mortals Overview
  • When you’re ready to understand how q runs in its native database environment, continue to: Introduction to kdb+
  • The full Q for Mortals series is available under Guides → Q for Mortals.

7. Tutorials and examples

Tutorials and examples that demonstrate the application of q and KDB-X concepts to practical data problems:

Next steps