Deephaven brings together your data in tables that change, updating in real time along with your data. The latest release, Deephaven v0.20.0, offers more table types to choose from, facilitating impressive visualizations from which to easily extract insights. We discuss the significant updates to Deephaven’s user guide below. Use our how-to guides to jumpstart your data journey.
This release includes three new table types:
- Tree tables – a hierarchial table that provides a convenient way to display your data by allowing you to expand or collapse “branches” of the table as needed. When fully collapsed, the “tree” component resembles a navigation menu: clicking on the arrow in a root row opens the data for that particular branch.
- Roll-ups – a hierarchical table that allows users to reduce a large, detailed data set to show only certain, customized aggregated column values.
- Input tables – user-modifiable database tables. Much like a spreadsheet, Input Tables are used to store user-provided data, which can then be used to drive calculations. Data in Input Tables can be added, changed, or removed via query or in the UI.
In addition to newly available table types, this release provides more ways to adjust your table layout.
Using Layout Hints, you can:
Table snapshotting has been re-engineered, so snapshot_history has been replaced with the more efficient snapshot_when method.
The new slice filter returns a table that is a subset of another table based on row positions.
The previous release added type inference. We’ve published a new concept guide on Python data types. Understanding how to manage data types in queries leads to cleaner, faster, and more reusable code.
Two new guides show you how to:
Keep tuning in each week for our developer demos. See Column Grouping in action, as well as a preview of the new remote user C++ API, which takes the existing C++ client library and leverages it with Python.
Our Slack community continues to grow. Join us!
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