app

Big Data in Python

Dash Enterprise > Enterprise AI Features > Big Data in Python

Dash Enterprise is your front end for horizontally scalable, big data computation in Python.

From Spark to Snowflake, Dask to Datashader...the Python "big data" tech stack has never been more varied or robust.

Dash Enterprise supports turnkey connections to the most popular "big data" back ends for Python, including Vaex, Dask, Datashader, RAPIDS, Databricks (PySpark), Snowflake, Postgres, and Salesforce.

In addition, Dash Enterprise ships with battle-tested, plug-and-play demos for best leveraging Dash with each of these technologies.

Scroll below to demo the latest in Python HPC through Dash user interfaces.

Get pricing

Vaex
Dask
Datashader
RAPIDS
Databricks
Snowflake
Postgres
Salesforce

Vaex

Vaex is a Pandas-like library that can operate on vastly larger datasets through out-of-core memory mapping.


If you’re working with data that is too large to fit in memory, but you don’t want to go through the hassle of setting up Spark or Dask, give Vaex a try.

This Dash app uses Vaex to explore 117 million rows of data (7GB) in real-time.

RAPIDS + Dash:

Visualize insanely big data insanely fast

📈 Interactively visualize 300 Million+ datapoints in a web browser with a single GPU.

🌎 GPU acceleration lets analysts zoom between global, national, and individual level data in real-team.

🐼 In this demo, CPU aggregation (Pandas) is >20x slower than GPU aggregation (RAPIDS cuDF).

🎥 Watch an aggregation in this demo that takes Pandas 98 seconds and RAPIDS cuDF 0.59 seconds.

“Modern AI cannot exist without access to
high-performance computing.”

Foteini Agrafioti Chief Science Officer at RBC and head of Borealis AI

We're proud to partner with these best-in-class big data Python solutions.

See Dash in action

Sign up for our next Dash Live Weekly demo session to learn more about our Dash Enterprise offering, including industry applications and all the latest tips and features on how to operationalize your data science models.

Please fill all *required* fields