(dTosh/Shutterstock)
As AI-driven algorithms become increasingly complex, the demand for scalable solutions that integrate powerful analytical engines with machine learning libraries has surged. KX, a performance analytical database for AI, has responded to this need by enhancing PyKX, its Python-first interface for kdb+, with a new hybrid architecture.
PyKX 3.0 merges kdb+’s processing power with Python’s ML capabilities. The company claims that developers can use the platform to build advanced AI-driven applications and analytics without compromising on speed or scalability.
In May 2023, KX had open-sourced PyKX, making its kdb+ time-series database and q programming language accessible to the global Python developer and data science communities. This move led to over 400,000 downloads across various distribution channels, highlighting PyKX’s rapid adoption.
A key factor in this success is Python’s widespread popularity among data scientists and developers. By integrating seamlessly with Python’s existing tools and libraries, PyKX enabled users to quickly tap into KX’s advanced analytics capabilities, driving its growth and making it an attractive solution for real-time and historical data analysis.
“If you’re looking for a single piece of technology that can do both historical and real-time analysis, kdb+ is the de facto standard in the trading industry,” said Emanuele Melis, Principal Data Engineer at Talos and KXperts member. “We went through a period where firms were looking for the next big technology that would revolutionize trade analytics, but what we’ve realized is that kdb+ and Python are the two pillars of modern quantitative research.”
Since its initial release in 2022, PyKX has allowed development to extend the power of kdb+ to Python. However, the company had received several requests to expand the library. After six months of development, the PyKX team unveiled enhancements and upgrades to the platform.
The two major upgrades include the PyKX query API update to support Python first syntax and the addition of a streaming module for high-velocity data ingestion and persistence. PyKX shared that “5% of tasks can be done fully via Python, removing programming language knowledge gaps.”
Additionally, enhancements in this update include the migration of beta features introduced in PyKX 2.x to full production. This includes database creation and management, remote function execution, and multi-threaded use of PyKX. Users also get more granular control over the IPC reconnection process and access to support for the Python help command on all PyKX keywords.
According to Conor McCarthy, lead architect at KX, the PyKX was built to “better serve the developer community” by expanding access to the power of kdb+ and q. Originally designed for q developers, the PyKX platform has evolved to support the needs of millions of Python users.
“At the outset of this release, we aimed to provide all library users, newcomers, and those supporting its development with enhancements that fit into their day-to-day operations but equally allow for new use cases and new users to be onboarded.”
McCarthy emphasized that the goal of the new update is to enable Python developers to leverage kdb+ alongside popular ML and AI tools, all while maintaining their existing workflows. “We don’t want to change the way that they think, just the way that they work.”
In recent years, Python has seen growing support across the big data and analytics ecosystem, as many platforms have worked to integrate it more effectively, making it a first-class language for data engineering and analysis. PyKX has capitalized on this trend by enabling Python developers to leverage kdb+ for high-performance data analytics.
Related Items
Anaconda Hooks Python, R Libraries Into Snowflake Data Science Notebook
Python Popularity Persists, AI Drives PyTorch
Three Reasons Python Is The AI Lingua Franca
Source link
lol
