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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. 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. Have questions?...reach out on our community slack channel at https://deephaven.io/slack . Get started with examples on our website at https://deephaven.io/ #Deephaven #LearningSessions #demo #odsc