KDB Developer

INTELLECT MINDS PTE. LTD.; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job description

The Kdb database, developed by KX, is widely used in the financial services industry, particularly by major investment banks, for high-speed, real-time data processing and analytics. Kdb+ is a high-performance, column-based, in-memory database optimized for time-series data analysis, commonly used in financial services for handling large real-time and historical datasets. It is developed by KX Systems and uses the q programming language, which is designed for high-speed data manipulation.

Use Cases

Kdb+ is optimized as a high-performance time-series database, making it ideal for the demanding, data-intensive environments within capital markets. Common applications include:

• High-Frequency Trading (HFT): Processing massive volumes of market data (trades and quotes) in milliseconds to enable rapid, automated trading decisions.
• Risk Management: Calculating and monitoring risk exposure across portfolios in real time, allowing firms to quickly respond to market changes.
• Quantitative Research and Analytics: Providing a single platform for both real-time and historical data analysis, crucial for developing trading models and strategies.
• Surveillance and Fraud Detection: Analyzing data streams in real time to identify unusual patterns or potential fraudulent activities.
• Profit & Loss (P&L) Reporting: Offering instant feedback on the performance of trading positions for traders and analysts.

Key features

• Performance: Known for its speed, it processes and analyzes massive datasets in real-time by storing data primarily in RAM.
• Columnar and time-series focused: It stores data in columns, which is efficient for analytics, and is particularly strong at handling time-series data, a critical component for many financial applications.
• q programming language: Includes a built-in, vector-based language called "q," which is an expressive, high-performance language used for building data analytics solutions.
• Use cases: Widely adopted by major financial institutions for high-frequency trading, real-time analytics, and historical data storage and retrieval.

Kdb Insights Database

A more recent, distributed version of kdb+ designed for scalability with features like scalable query routing and temporal storage tiering.