Description

Introduction

Modern enterprises rely heavily on RESTful services and web-based integrations to exchange real-time and batch data across systems. While kdb+ is widely known for ultra-fast time-series analytics, integrating it with REST APIs and web applications unlocks powerful use cases such as:

1. Real-time financial dashboards
2. Data ingestion from external services
3. Microservices architecture integration
4. Cloud-native deployments
5. Web-based analytics platforms

This training provides a comprehensive, hands-on approach to building, consuming, and deploying REST APIs with kdb+ using q. Participants will learn how to expose kdb+ data via HTTP endpoints, integrate with external web services, and design scalable API-driven architectures.

Prerequisites

1. Basic understanding of kdb+ & q
2. Knowledge of:
   1. Tables, dictionaries, and queries in q
   2. IPC concepts in kdb+
3. Basic understanding of:
   1. REST architecture principles
   2. HTTP methods (GET, POST, PUT, DELETE)
   3. JSON data format
4. Familiarity with Linux/command line (recommended)
5. Basic programming knowledge (Python/JavaScript is helpful but not mandatory)

Table of Contents

Module 1: REST Fundamentals & Web Architecture

1. Introduction to REST architecture
2. HTTP protocol overview
3. HTTP methods and status codes
4. Stateless communication principles
5. JSON and data exchange formats
6. REST vs IPC in kdb+

Module 2: HTTP Capabilities in kdb+

1. Built-in web server capabilities in kdb+
2. Using .z.ph (HTTP handler)
3. Understanding .z.pp and request parsing
4. Handling GET and POST requests
5. Query string parameters
6. Response formatting

Module 3: Building REST APIs with q

1. Designing API endpoints
2. Mapping URLs to q functions
3. Returning data as JSON
4. Custom HTTP response headers
5. Error handling and status codes
6. Logging and request tracking

Hands-on Lab:
Create a REST API to fetch time-series data from a kdb+ table.

Module 4: JSON Handling in q

1. JSON parsing in q
2. Serializing tables to JSON
3. Nested JSON structures
4. Performance considerations
5. Handling large payloads

Module 5: Consuming External REST APIs

1. Making HTTP requests from q
2. Calling external APIs
3. Authentication handling
4. Parsing API responses
5. Data ingestion workflows

Hands-on Lab:
Integrate kdb+ with a third-party market data API.

Module 6: Web Application Integration

1. Integrating kdb+ with frontend applications
2. Connecting to JavaScript/React dashboards
3. Real-time data updates
4. WebSocket vs REST considerations
5. Microservices integration patterns

Module 7: Security & Authentication

1. API authentication methods
2. Token-based authentication (JWT concepts)
3. SSL/TLS setup
4. Access control strategies
5. Protecting sensitive financial data

Module 8: Performance & Scalability

1. Optimizing API response time
2. Caching strategies
3. Load balancing concepts
4. Scaling API-enabled kdb+ services
5. Monitoring and logging

Module 9: Deployment & Production Setup

1. Deploying HTTP-enabled kdb+ processes
2. Reverse proxy configuration (NGINX overview)
3. Dockerizing kdb+ API services
4. Cloud deployment considerations
5. CI/CD integration