ime-Series Databases and Python Integration with Insights from a KX Developer Advocate

In this enlightening conversation, a veteran developer advocate from KX unfolds critical insights about the compelling database, KDB Plus. Journeying through her personal evolution from an apprentice to an advocate, she delves into the launch and progression of KDB Plus and the cost columnar structure that enhances its query efficiency.

This is an interview and webinar presentation with Michaela Woods who is Developer Advocate at KX.

Beyond the basics, she highlights the extensive resources available for learning KDB Plus — from books to online training academies – and emphasizes the significant role of community spaces such as forums and online platforms for knowledge share and query solution.

Ger your free trading tech books here books2 - QUANTLABS.NET

More details here

Why KX kdb+ and Q Should Be Your Next Financial Power Tools - QUANTLABS.NET

The discussion takes a turn, exploring KX Academy's course structure designed to equip learners with fundamental to advanced understanding of handling massive time-series data and creating custom functions. Demonstrating the sandbox feature, Michaela highlights the beauty of learning in a hosted environment without the need for installations. Michaela concludes with a glance at their advanced courses, tailor-made for individuals wanting to master KDB Plus.

Moving on, the conversation introduces a more modern development – PyKx. Shedding light on PyKx's rising popularity, it describes how Python integration is expanding KDB Plus's accessibility to software applications with its enticing interfaces for new users, without replacing the underlying Q language.

Discussion further covers the inclusive certification programs by KDB and its successful implementation in the manufacturing sector. Lastly, it dives into the recently launched developments: KDB AI and KDB Insights, exploring how they are innovatively reshaping data storage, retrieval, and cloud-based workloads.

This comprehensive discussion is designed to equip developers and beginners alike to leverage KDB Plus and PyKx for efficient time-series data handling and enhanced data analysis.