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Version: Python

Deephaven Tutorial

Learn how Deephaven works

The goal of this tutorial is to help you create a mental model for how Deephaven works and to introduce you to its capabilities and workflows.

You will build analytics and applications with both updating (streaming) and static (batch) data sets, become familiar with the Deephaven IDE experience, apply your Python functions to your data, build a dashboard, create a notebook, and build and launch a stand-alone application. With a bit more time, you’ll then develop a Python client application that uses the API to receive data from your server application.

0. Quick Start

Set up Deephaven in your Docker environment with pre-built images.

1. Ingest static data

Import CSVs from URLs. Read an example CSV and Parquet file.

2. Ingest real-time streams

Learn about Kafka-importers and see updating tables in your browser.

3. Create columns & merge tables

Examine metadata, cast types, create new columns, and combine static and updating tables.

4. Manipulate & aggregate data

Do calculations and script aggregations that update in real time.

5. Filter, join, & as-of join

Perform as-of and classic joins based on a variety of key combinations.

6. Plot data via query or the UI

Create dynamic plots that update in real time.

Snapshot ticking data and write to Pandas dataframes, CSVs, and Parquet.