Crash Course Overview

This crash course takes you from starting a Deephaven server to building real-time queries, plots, and UIs. Each chapter builds on the ones before it, so work through them in order.


Get started

Start a Deephaven server with Docker or pip, open the IDE, and get familiar with the console and workspace. Dive in and unlock the potential of this powerful platform.

Architecture overview

Learn how Deephaven's tables, table operations, engine, and user interface fit together, starting from the central idea that everything is a table.

Create tables

Learn to create static tables from scratch, read CSV data from a file or URL into a table, create ticking tables that update in real time, and replay static data as a ticking table.

Table operations

Learn the table operations that appear in almost every query: add, select, and drop columns; filter and sort rows; group and aggregate data; and merge and join tables.

Time and calendars

Work with date-time types, time zones, time arithmetic, and business calendars.

Query strings

Deephaven query strings are the primary way of expressing commands directly to the Deephaven engine. They tell the engine what to compute, and the engine turns them into filters and formulas, compiling code where needed. Learn the query-string syntax that table operations use to filter rows and compute new columns, and how to call built-in and user-defined functions from inside a query string.

Recipes, not loops!

Understand why Deephaven uses declarative recipes instead of loops. Learn the paradigm shift that unlocks automatic real-time updates: a recipe lets query results update in real time as new data arrives, without re-running your code.

Python integrations

Deephaven empowers Python developers by providing efficient integrations with popular Python libraries. Convert Deephaven tables to and from pandas and NumPy, and learn the limits of static Python data structures when data updates in real time. Then apply machine learning models to ticking data with table listeners and deephaven.learn.

Real-time plots

Whether your data is static or updating in real time, Deephaven can plot it. Build basic plots, plots with multiple axes, and subplots with the built-in plotting API. The chapter also points you to Deephaven Express, the recommended plotting library.

deephaven.ui

Use the deephaven.ui Python package to build interactive UIs and dashboards that present your tables and plots.

Data I/O

Data I/O is mission-critical for any real-time data analysis platform. Read data into Deephaven and write it back out in common formats and systems, including CSV, Parquet, Kafka, Iceberg, and pandas DataFrames.

Configure your Deephaven instance

Take your Deephaven instance beyond the defaults: change authentication, choose a deployment, install Python packages, set memory limits, and keep your instance up to date.

Wrapping up

Find out where to go next in the documentation and where to get help from the Deephaven community.