Description

Introduction

In today’s data-driven environment, the ability to transform high-frequency and time-series data into actionable visual insights is critical for decision-making. Visualization & Dashboarding with kdb+ Data focuses on designing real-time and historical dashboards powered by kdb+ and q.

This training enables participants to extract, aggregate, and visualize kdb+ data efficiently using q queries, integrate with visualization tools, and build interactive dashboards suitable for trading, risk monitoring, operations, and analytics environments.

Participants will learn how to connect kdb+ with BI tools, web frameworks, and custom visualization layers while ensuring high performance and scalability.

Prerequisites

1. Basic understanding of kdb+ architecture
2. Working knowledge of q language fundamentals
3. Experience with querying tables in q
4. Understanding of time-series data concepts
5. Basic familiarity with BI/visualization tools

Table of Contents

Module 1: Role of Visualization in kdb+ Ecosystem

1. Importance of dashboards in financial & time-series analytics
2. Real-time vs historical dashboards
3. Common dashboard architectures with kdb+

Module 2: Preparing kdb+ Data for Visualization

1. Data aggregation techniques in q
2. Pivoting and reshaping data
3. Time bucketing and windowed calculations
4. Pre-computed vs on-demand analytics
5. Optimizing queries for dashboard responsiveness

Module 3: Connecting kdb+ to Visualization Tools

1. Using ODBC/JDBC connectors
2. REST APIs with kdb+
3. IPC-based data feeds
4. Exporting data to CSV/JSON
5. Integration with Python/R for visualization

Module 4: Popular Visualization & BI Integrations

1. Connecting kdb+ with Power BI
2. Using Tableau with kdb+
3. Grafana dashboards with time-series data
4. Web-based dashboards (JavaScript frameworks)
5. Python dashboards (Plotly, Dash)

Module 5: Real-Time Dashboarding

1. Streaming data in kdb+
2. Publish/subscribe model
3. Tick architecture integration
4. Real-time metrics computation
5. Handling large data volumes in dashboards

Module 6: Building Custom Dashboards

1. Backend query layer design
2. API design for dashboards
3. Caching strategies
4. User-level filtering & parameterization
5. Drill-down analytics

Module 7: Performance & Scalability

1. Minimizing query latency
2. Partition-aware querying
3. Load balancing & multi-process architecture
4. Caching and materialized views
5. Best practices for production dashboards

Module 8: Security & Governance

1. Access control in kdb+
2. Securing API endpoints
3. Data masking strategies
4. Monitoring and auditing dashboard usage

Module 9: Hands-On Capstone Project

1. Build a real-time trading dashboard
2. Historical analytics dashboard
3. KPI monitoring dashboard
4. Performance optimization exercise