📃
v5.3.2
  • Welcome to OmniSci Documentation
  • Overview
    • OmniSci Overview
    • Release Notes
  • Installation and Configuration
    • System Requirements
      • Hardware Reference
      • Software Requirements
    • Installation
      • Installing on CentOS
        • CentOS/RHEL Enterprise Edition CPU with Yum
        • CentOS/RHEL Enterprise Edition GPU with Yum
        • CentOS/RHEL Open Source CPU with Yum
        • CentOS/RHEL Open Source GPU with Yum
        • CentOS/RHEL Enterprise Edition CPU with Tarball
        • CentOS/RHEL Enterprise Edition GPU with Tarball
        • CentOS/RHEL Open Source CPU with Tarball
        • CentOS/RHEL Open Source GPU with Tarball
      • Installing on Ubuntu
        • Ubuntu Enterprise Edition CPU with Apt
        • Ubuntu Enterprise Edition GPU with Apt
        • Ubuntu Open Source CPU with Apt
        • Ubuntu Open Source GPU with Apt
        • Ubuntu Enterprise Edition CPU with Tarball
        • Ubuntu Enterprise Edition GPU with Tarball
        • Ubuntu Open Source CPU with Tarball
        • Ubuntu Open Source GPU with Tarball
      • Installing on Docker
        • Docker Enterprise Edition CPU
        • Docker Open Source CPU
        • Docker Enterprise Edition GPU
        • Docker Open Source GPU
      • Installing on AWS
      • Installing on GCP
      • Installing on Azure
      • Upgrading OmniSci
      • Uninstalling OmniSci
    • Services and Utilities
      • Using OmniSci Services
      • Using OmniSci Utilities
    • Configuration Parameters
    • Security
      • Roles and Privileges
      • Connecting to OmniSci Using SAML
      • Implementing a Secure Binary Interface
      • LDAP Integration
    • OmniSci Distributed Configuration
  • Loading and Exporting Data
    • Supported Data Sources
      • Kafka
      • Using Immerse Data Manager
      • Importing Geospatial Data
    • Command Line
      • Loading Data with SQL
      • Exporting Data
  • SQL
    • Data Definition (DDL)
      • Datatypes
      • Users and Databases
      • Tables
      • Views
    • Data Manipulation (DML)
      • SQL Capabilities
      • Geospatial Capabilities
      • Functions and Operators
      • OmniSci Reserved Words
      • OmniSci SQL Extensions
  • Immerse
    • Introduction to Immerse
    • Working with Dashboards
      • Dashboard List
      • Creating a Dashboard
      • Configuring a Dashboard
      • Duplicating and Sharing Dashboards
    • Measures and Dimensions
    • Using Filters
    • Multilayer Charts
    • SQL Editor
    • Customization
    • Immerse Chart Types
      • Overview
      • New Combo
      • Bar
      • Bubble
      • Choropleth
      • Combo
      • Geo Heatmap
      • Heatmap
      • Histogram
      • Line
      • Linemap
      • Number
      • Pie
      • Pointmap
      • Scatter Plot
      • Stacked Bar
      • Table
      • Text Widget
  • Data Science
    • OmniSci Data Science Foundation
    • Installation and Configuration
    • Using OmniSci with JupyterLab
    • Introduction to Ibis
    • Examples
      • Interactive Data Exploration with Altair
      • Forecasting with Prophet
      • Machine Learning with RAPIDs
      • Scheduled workflows with Prefect
      • Quick ML Applications with Streamlit
  • APIs and Interfaces
    • omnisql
    • Thrift
    • JDBC
    • ODBC
    • Vega
      • Vega Tutorials
        • Vega at a Glance
        • Getting Started with Vega
        • Getting More from Your Data
        • Creating More Advanced Charts
        • Using Polys Marks Type
        • Vega Accumulator
        • Using Transform Aggregation
        • Improving Rendering with SQL Extensions
      • Vega Reference Overview
        • data Property
        • projections Property
        • scales Property
        • marks Property
      • Migration
        • Migrating Vega Code to Dynamic Poly Rendering
        • Migrating Polygon Rendering for Custom Clients Upgrading to OmniSci 4.0
      • Try Vega
    • RJDBC
    • pymapd
    • mapd-connector
  • Tutorials
    • Loading Data
    • Using OmniSci Immerse
    • Hello OmniSci
    • Creating a Kafka Streaming Application
    • Getting Started with Open Source
    • Architecture Overviews
  • Troubleshooting
    • FAQ
    • Optimizing OmniSci
    • Known Issues and Limitations
    • Logs and Monitoring
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  1. Data Science

Using OmniSci with JupyterLab

PreviousInstallation and ConfigurationNextIntroduction to Ibis

Last updated 4 years ago

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OmniSci Enterprise Edition comes with a fully integrated version of JupyterHub. This provides a secure, multi-user notebook environment for data exploration with OmniSci, and is the primary user interface for the OmniSci Data Science Foundation.

You can quickly switch from visual data exploration, to a Data Science environment preloaded with useful open source libraries and tools that work with OmniSci transparently.

You can access JupyterLab, the next generation Jupyter notebook UI from within Immerse via a button located on the dashboard title

In addition, you can also launch JupyterLab from SQL Editor. In this case, a notebook is opened up with the query wrapped in an Ibis expression.

OmniSci Enterprise allows you to control which users have access to JupyterLab and Data Science tools. This is managed as a separate privilege under the OmnISci role-based permissions model. Refer to the installation instructions for how to set this up for specific users

If you are an OmniSci open source edition user, you will not have access to Immerse, but you can still explore OmniSci with the Data Science Foundation tools.

Using Jupyterlab with OmniSci Open Source Edition will require you to connect to OmniSci explicitly from within a Jupyter notebook cell, using Ibis or pymapd connection syntax. Refer to the Ibis or pymapd examples for further details.

Using the Anaconda Package Manager

If you are using the Anaconda package manager, you can install the set of python tools for OmniSci like th

conda install -c conda-forge omnisci-pytools

Using the OmniSci Docker Container for Jupyter tools

You can download a pre-built Docker container for these tools at [insert link], and simply start up these tools as a standalone container.

Coming soon

Tools and Utilities

OmniSci provides a collection utilities to work with JupyterLab.

This is a collection of useful functions and Jupyter cell magics that allows running commands that are typically possible from omnisql from inside a notebook environment

Access JupyterLab from within Immerse (OmniSci EE)
Upon running a query in SQLEditor, the 'Send to Jupyter' icon becomes visible