📄️ Get Started
A Crash Course in Deephaven is your backpack guide through the world of real-time data analysis using the Deephaven data engine. This guide provides a broad - but clear and technically informative - overview of Deephaven’s capabilities. Let's dive in and unlock the potential of this powerful platform.
📄️ Architecture Overview
Deephaven's power is largely due to the concept that everything is a table. Think of Deephaven tables like DataFrames, except that they support real-time operations! The Deephaven table is the key abstraction that unites static and real-time data for a seamless, integrated experience. This section will discuss the conceptual building blocks of Deephaven tables before diving into some real code.
📄️ Create Tables
Static tables
📄️ Table Operations
This section will cover some table operations that appear in almost all queries. These table operations use query strings extensively, which are discussed in detail in the next section.
📄️ Query Strings
Deephaven query strings are the primary way of expressing commands directly to the Deephaven engine. They translate the user's intention into compiled code that the engine can execute. These query strings can contain a mix of Java and Groovy code and are the entry point to a universe of powerful built-in tools.
📄️ Real-time Plots
Whether your data is static or updating in real time, Deephaven supports plotting in Groovy via multiple its built-in plot package.
📄️ Data I/O
Data I/O is mission-critical for any real-time data analysis platform. Deephaven supports a wide variety of data sources and formats, including CSV, Parquet, Kafka , and more. This document covers those formats in Deephaven.
📄️ Configure your Instance
This last section covers configuration details needed to take your Deephaven instance beyond the defaults.
📄️ Wrapping Up
Where to go from here