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DASH ENTERPRISE
Big Data Pipelines
Dash Enterprise is your front end for horizontally scalable, big data computation in Python.

About Big Data Pipelines
Since Python connects to any database, it's easy to empower business users with Dash apps that connect to your databases, query data, perform advanced analytics in Python, and even write back results. As the world's fastest growing language, every database vendor offers a Python connector library (see table below). That's great news for Dash users building analytics apps that connect to databases.
For example, your Dash app might connect to a Snowflake database with Python's Snowflake connector, read customer reviews, perform NLP sentiment analysis, then email a PDF report with Snapshot Engine.
Dash Enterprise ships with battle-tested, plug-and-play Dash app demos for connecting to Snowflake, Databricks, Postgres, Redis, BigQuery, Salesforce, and Redshift. These demos show best practices for connecting and querying databases in Python. Scroll below to demo Python Dash apps that connect to today's most back-end popular databases.
Connector Templates
Connect Any Data Pipeline
Databricks
Vaex
Dask
Datashader
Snowflake
Postgres
Salesforce
Secrets Manager

Connector Templates
Dash Enterprise ships with plug & play Dash app templates for connecting to these and other data services in Python. These templates demonstrate best practices such as:
- Using the Dash Enterprise Jobs Queue
- Connection pooling
- Preventing SQL injection attacks
- Reading SQL query results into Pandas dataframes
- Secure database authentication with the Dash Enterprise Secrets Manager

Connect Any Data Pipeline.
In Python, there is always a library to connect to any database, datastore, API, or file format. With Dash Enterprise, your Dash apps can connect directly to your datastore or API in Python. You never have to migrate your data (though Dash Enterprise has an onboard Postgres data cache if you wish to do so). Dash Enterprise ships with tested Dash app templates for connecting to most popular databases and data services.

Databricks
Databricks is the company and commercial platform behind Spark and PySpark.
Dash apps that use PySpark and are deployed on Dash Enterprise can call out to Databricks Spark clusters through the Dash Enterprise Job Queue.
Unlike Dask or RAPIDS, Spark does not work with Datashader, so there is no way to build interactive dashboards with PySpark that can visualize ~100 million rows of data and upsample or downsample in real-time.

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.

Dask
Dask is the de facto parallel computing library for Python. Dask is gaining popularity over PySpark because of its relatively low overhead to set up.
If you have a machine with multiple cores and a numerical computing problem that can be parallelized, give Dask a try.
Dash and Dask also work wonderfully with Datashader.
This Dash app uses Dask and Datashader to explore 40 million rows of data in real-time.

Datashader
Datashader is an open-source Python library for server-side rendering of big data visualizations.
Dash apps integrate closely with Datashader to visualize big data. When zoomed out, Dash uses Datashader to render the entire “big data” visualization server-side. When zoomed in, Dash switches to Plotly graphing for interactive, high resolution data exploration.
Dash + Datashader can be scaled to 100s of millions of points with Dask and RAPIDS — see the Dask and RAPIDS demos for examples.
This Dash app uses Datashader to visualize 1 million time series points in 300 lines of Python.

Snowflake
Snowflake is a cloud-only, distributed commercial data warehouse with drivers for Python and R.
BI tools like PowerBI or Tableau are typical front ends for Snowflake — use Dash when you need an AI front end for NLP, computer vision, predictive analytics, or deep learning using data stored in Snowflake.

Postgres
If you’re managing a terabyte or less of tabular data, you may not need Spark, Dask, or Snowflake. Vaex or the Postgres Python driver will do!
Dash Enterprise ships with onboard Postgres and Redis databases to store and cache data for your Dash apps. Both Postgres and Redis are fast and easy to access from Python, R, and Julia.
This Dash app queries NYC complaints stored in Dash Enterprise’s onboard Postgres database.

Salesforce is a cloud-based customer relationship management system for marketing, sales, commerce, and service.
Dash Enterprise includes built-in Salesforce Embedding Middleware integration. You'll be able to use the platform for Salesforce data processing and authentication, as well as its infrastructure for effortless simultaneous application viewing.

Secrets Manager
Secure, store, and tightly control access to tokens, passwords, and API keys that your Dash apps use. Secrets Manager enables secure access to remote data stores and cloud services from your Dash apps, without exposing these secrets in your Dash app code.

We are not speaking just about reports/dashboards. Dash helps us to organize and combine our data and make it available for a wider spectrum of colleagues, who may require in the future some kind of interaction, such as running finite element simulations for specific parameter combinations.
Senior Simulation Engineer at High-Performance Automobile Manufacturer

Connect your Dash apps to all major data warehouses.
- Python, R, and Julia supports best-in-class, open-source connection libraries for Snowflake, Amazon Redshift, IBM DB2, Google BigQuery, PostgreSQL, and Azure SQL Data Warehouse, making it simple to connect these data services to your Dash apps. Dash Enterprise comes with connection examples for each of these data warehouses, so you can easily copy/paste the code into your own Dash apps.
- Use Pandas dataframes in Data Science Workspaces or your Dash application code to rapidly filter and visualize data warehouse query results.
- Use the Dash Enterprise Job Queue to sideline long-running data warehouse queries and accelerate your Dash application performance.
Python connects to any database.
This makes it simple to connect your Dash apps to any database, perform AI & ML routines in Python on the data, then deliver these insights to business users as Dash apps.
Database | Python connector library name | Python library downloads per month | Installation command | Dash app example | Documentation |
---|---|---|---|---|---|
Databricks | Databricks SQL Connector for Python | 1.5M+ | pip install databricks-sql-connector | Dash-Databricks App | Python SQL Connector | Python SQL |
MySQL | mysql-connector-python | 10M | pip install mysql-connector-python | Contact sales | Querying data |
Postgres | pyscopg2 | 14M | pip install psycopg2-binary | Contact sales | Basic usage |
Snowflake | snowflake-connector-python | 18M | pip install snowflake-connector-python | Sentiment analysis app | Querying data |
Google BigQuery | google-cloud-bigquery | 44M | pip install google-cloud-bigquery | Contact sales | Perform a query |
Amazon RedShift | redshift_connector | 15M | pip install redshift_connector | Contact sales | Basic example |
MS Azure SQL | pyodbc | 19M | pip install pyodbc | Contact sales | Azure SQL Database libraries for Python |
Redis | redis | 26M | pip install redis | Redis on Dash Enterprise | redis-py |
MongoDB | pymongo | 19M | pip install pymongodb | Contact sales | Tutorial |
Elasticsearch | elasticsearch | 16M | pip install elasticsearch | Contact sales | Python Elasticsearch Client |
Postgres, MySQL, SQLite, Oracle, and MS SQL | sqlalchemy | 50M | pip install sqlalechemy | Contact sales | SQL Alchemy Dialects |
Salesforce | simple-salesforce | 4.9M | pip install simple-salesforce | Salesforce Dash app | Simple Salesforce |
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