Description

Introduction

In today’s data-driven enterprises, real-time data processing is critical for industries such as financial services, telecom, IoT, energy, and cybersecurity. Building Real-Time Data Pipelines with kdb+ is a comprehensive hands-on training designed to help professionals design, implement, optimize, and scale high-performance streaming data systems using kdb+ and the q language.

This course focuses on real-time ingestion, stream processing, tick architecture, distributed systems, historical data management, and high-performance analytics. Participants will learn how to architect production-grade pipelines capable of handling millions of events per second with low latency and high reliability.

By the end of this training, learners will be able to build scalable, fault-tolerant, and high-throughput data pipelines using kdb+ best practices.

Prerequisites

1. Basic understanding of databases and data structures
2. Familiarity with Linux/Unix command line
3. Basic programming knowledge
4. Prior exposure to q/kdb+ fundamentals
5. Understanding of real-time or streaming concepts

Table of Contents

Module 1: Fundamentals of Real-Time Data Systems

1. Overview of real-time vs batch processing
2. Event-driven architectures
3. Latency, throughput, and scalability concepts
4. Industry use cases (Finance, IoT, Market Data)

Module 2: kdb+ Architecture Deep Dive

1. Overview of kdb+ architecture
2. In-memory vs on-disk databases
3. Process roles: Tickerplant, RDB, HDB
4. Inter-process communication (IPC)
5. Multi-threading and parallelism in q

Module 3: Building a Tick Architecture

1. Understanding tickerplant design
2. Publishing and subscribing to feeds
3. Real-time data capture and logging
4. Intraday database (RDB) implementation
5. End-of-day data persistence to HDB
6. Data partitioning strategies

Module 4: Data Ingestion & Feed Handlers

1. Designing feed handlers in q
2. Parsing and normalizing streaming data
3. Handling high-frequency data
4. Schema design for real-time systems
5. Fault tolerance and data recovery strategies

Module 5: Real-Time Stream Processing

1. Real-time aggregations
2. Sliding windows and time-based calculations
3. Incremental analytics
4. Stateful vs stateless processing
5. Alerting and rule-based processing

Module 6: Performance Optimization

1. Memory management in kdb+
2. Optimizing table schemas
3. Column attributes (sorted, parted, grouped)
4. Query performance tuning
5. Benchmarking techniques

Module 7: Distributed & Scalable Pipeline Design

1. Scaling tickerplants
2. Load balancing strategies
3. Horizontal vs vertical scaling
4. Data sharding and partitioning
5. High availability setup

Module 8: Integration & APIs

1. Connecting kdb+ to external systems
2. REST and WebSocket integration
3. Python/R integration
4. Messaging systems integration (Kafka-style architectures)
5. Exporting real-time data streams

Module 9: Monitoring, Logging & Production Deployment

1. Logging strategies
2. Monitoring system health
3. Latency tracking and diagnostics
4. Deployment best practices
5. Disaster recovery planning

Module 10: Hands-On Capstone Project

1. Design a real-time market data pipeline
2. Implement tickerplant + RDB + HDB
3. Build real-time analytics dashboard backend
4. Performance optimization and stress testing
5. Production-readiness checklist