Abstract: 

Wouldn’t it be great if Python pandas worked with real-time, dynamic data? Or if publishing updating, derived data to apps and dashboards was easy? Deephaven Data Labs has an open solution exists today. Deephaven is a general-purpose data system built from the ground up to make working with real-time data easy -- on its own or in combination with large batch loads. Spun out of Wall Street trading, Deephaven provides intuitive, open, community tools and experiences for...

 working with data from Kafka, redpanda, other message queues, and your custom streaming
sources;
 performing table operations that update in real-time as new data arrives;
 integrating your custom or 3rd-party Python libraries with real-time data;
 applying data science, ML, and AI models on dynamic data;
 exploring and interrogating;
 creating real-time dashboards.

Session Outline:
This demonstration of Deephaven will show you how to:
1. Launch Deephaven from Jupyter and your Python IDE.
2. Access and manipulate Kafka and Parquet with just a few lines of code.
3. Filter, aggregate, join, select, and project ticking, updating tables in real-time.
4. Create and interact with table and plot widgets in web dashboards and Jupyter.
5. Integrate Deephaven with matplotlib, PyTorch, and other popular tools.

Bio: 

Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.