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Deephaven has solutions for real-time streaming tables spanning a broad array of technologies and sectors.

pandas and deephaven


Deephaven’s open-core query engine can be used for application development and real-time analytics with many popular Python packages and open source libraries, including NumPy, pandas, TensorFlow, PyTorch, and PyArrow. Its technology is used by companies in various industries, including capital markets for financial predictions and portfolio management and crypto for making investment decisions. More resources are listed below:


Optimize Deephaven Python queries by leveraging NumPy's speed, efficiency, and accessibility.


Leverage the most used library for data analysis and manipulation in Python to hold large data projects together like glue.


Utilize this open source machine learning framework to accelerate research and deploy products faster with real-time and big data.


Engage with one of the most well known open-source machine learning libraries for Python to easily build, train, and deploy machine learning models to solve real-world problems.


Leverage this development platform for analytics to easily store and move flat and hierarchical data.


Take advantage of SciKit-Learn’s intuitive and easy-to-use syntax to implement unsupervised learning models for regression, clustering, and classification.


Apply advanced processing routines to your data with this vast library of capabilities built to be used in conjunction with NumPy.


Capital Markets

Deephaven Enterprise cut its teeth crafting cutting-edge analytics for reliable financial predictions and portfolio management. Now Deephaven Community Core brings that same code to the rest of the world, in applications like Interactive Brokers.


Think smarter, not harder. Capitalize on analytics using real-time and historical data to make informed investment decisions for crypto or algorithmic trading.

Need some extra muscle?

Talk to our professional services team for help with custom integrations.