JupyterLab Extension for INFACore

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Introducing JupyterLab extension for INFACore

If you are a developer, data scientist, data engineer, or machine learning engineer, and you prefer low-code data integration capabilities for your diverse data management use cases, you can plug in INFACore into your JupyterLab environment and take your data management projects from ideation to deployment, in a real quick and cost-effective manner.

The JupyterLab extension for INFACore is a JupyterLab-based development environment for INFACore. The extension provides you with a user interface for INFACore that helps you interact with INFACore from within JupyterLab. Use the INFACore SDK for Python to execute code to manage data in the INFACore instance in JupyterLab.

INFACore helps you experience the power and functionality of Informatica Intelligent Cloud Services from your JupyterLab environment while allowing you to retain complete control over the front-end experience of your application.

You can manage data from disparate sources directly from your JupyterLab environment. Use INFACore to extract source data, transform the data, and then load it to your target.

Your data science project might require you to use INFACore to perform the following tasks:

Explore data that is already residing in your development environment.

Access a data source from JupyterLab to either fetch data into JupyterLab or to write data from JupyterLab to the required data endpoint.

Apply built-in functions to validate, standardize, and cleanse your data.

Use Intelligent Structure Models to parse semi-structured and unstructured data.

INFACore connects to your data source and executes the operations based on the configurations you apply.

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JupyterLab Extension for INFACore

JupyterLab Extension for INFACore

Introducing JupyterLab extension for INFACore

Introducing JupyterLab extension for INFACore