Deciding to forego the January blues, “Everything Everywhere all with kdb+/q” provided our Rebecca Kelly and Aoife Clarke the perfect opportunity to soak up some Spanish sun (for one of us anyway). Between tapas and timeseries; pastel del nata and PyKX; we’ve summarised our takeaways below for those who might have missed it.
Deciding to forego the January blues, “Everything Everywhere all with kdb+/q” provided our Rebecca Kelly and Aoife Clarke the perfect opportunity to soak up some Spanish sun (for one of us anyway). Between tapas and timeseries; pastel del nata and PyKX; we’ve summarised our takeaways below for those who might have missed it.
Data Science in q -Javier Sabo from BBVA kicked off the evening with an introduction to performing data science using q, showing his true scientist form by leveraging Jupyter Notebooks (with a q kernel). Javier’s introduction to central limit order books was the perfect starting point for any burgeoning financial data scientist and the deftly woven in-python graphics leveraging PyKX were a valuable addition and learning aid for those unfamiliar with the topic.
INQDATA’s Key Takeaways:
Real-Time Use Cases: Exploring Use Cases in kdb+ - Esperanza López Aguilera representing Squarepoint Capital was up next and provided a great architectural overview of a typical kdb+ implementation, covering each of the main processes. Somehow, she still found time to talk through some raw tick.q code and demonstrate a live running kdb+ architecture performing real-time sentiment analysis of a news feed – Esperanza is as efficient as kdb+ itself!
INQDATA’s Key Takeaways:
PyKX: A gateway drug into q -Jesús López-González, VP of Research at Habla Computing impressed with his talk on PyKX – if you’ve been watching the project, you’ll know Habla have been providing valuable contributions. Jesús used the example of training an ML model on traffic data to compare the Pythonic and Q-centric approaches, explaining each method's steps and highlighting their key efficiency gains. Code profiling was exalted as the solution to identifying areas for efficiency and finished with a call to action for others in the community to contribute to the project also.
INQDATA’s Key Takeaways:
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KDB.AI: vector databases in action - Alfonso Campo from KX presented KDB.AI to the room, combining his enthusiastic presentation style with great foundational explanations of more complex topics such as vector embeddings. The core message of Alfonso’s presentation revolved around KDB.AI’s unique ability to unify structured, unstructured, and time-series data under one roof. This was demonstrably proven in his captivating live demo, which not only offered practical examples but also explored potential limitations and pitfalls, including those associated with LLMs.
INQDATA’s Key Takeaways:
From Wall Street to Formula 1 - Finally, First Derivative’s Daniel Moreno Ray presented on the wide-ranging use cases and non-financial applications to which kdb+ is being applied. While our Rebecca Kelly has some prior experience with the Formula 1 use case (having had the pleasure of working on the original POC with Redbull), Daniel kept us, and the rest of the room engaged while highlighting some of the quirks of the racing industry that made kdb+ such a great fit (huge data volumes, restricted wind tunnel hours, limited compute times). Secrecy being what it is in that industry, we enjoyed the ingenuity of the demo leveraging data captured from a PlayStation game to show the types of analysis being performed by these racing teams.
INQDATA’s Key Takeaways:
This was an excellent, well organised event by Habla providing a holistic introduction to all things kdb+ and showcasing the extent of the wider community (while also a great test of our Spanish skills!) Truly “Everything Everywhere all with kdb+/q” and we eagerly look forward to their next event.
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