JupyterLab Extension for INFACore

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End-to-end quick tour

You are a data scientist and you want to explore the JupyterLab extension for INFACore to manage data from your JupyterLab environment.

To get started, perform the following tasks after you log in to INFACore:

Step 1. Set up the runtime environment

The runtime environment is the execution platform that runs the INFACore jobs.

First, let's install the agent on the machine that hosts your development environment.

In the

Runtime Environment

section, click

to install the agent on your machine.

The runtime environment downloads an agent locally on your machine and the status displays as up and running, as shown in the following image:

The name of the agent installed locally is displayed. The status shows as up and running.

Step 2. Connect to the data source

First, select and configure the data source to which you want to connect. You can select an existing connection or create a new connection to connect to your data source.

In the

Connect to Data Sources

section, click the

Data Source Type

tab, and then select the data source from the list.

You can also search for the data source from the list.

You can view the list of data sources that you can choose from.

On the

Connections

tab, select an existing connection for the data source from the list, or create a new connection to the data source.

To create a new connection, click the + icon, and then specify the details for the data source that you want to connect to.

In the example, the selected data source is Snowflake.

For example, if you configure a Snowflake connection, enter a name for the connection, select the authentication method, and enter the Snowflake account details.

The following image shows the properties for a Snowflake connection:

The Snowflake connection properties dialog box displays, where you need to enter the Snowflake account details to connect to Snowflake.

When you create and save a new connection, that connection displays in the connection list.

Select the required connection and perform one of the following actions:

To add the connection to the code cell, select the

icon, and provide a variable name for the connection to display in the Python code.

The selected connection is added to the Python code.

When you click add to code cell, the connection code is added to the Python code.

To edit the connection, select the

icon, edit the connection details, and save the connection.

To test the connection, select the

icon.

The Python code for testing the selected connection displays.

Run the code to test if you can connect to Snowflake.

Step 3. Explore the data

After you configure the data source, you can configure functions on your data to perform the following operations:

Read, write, convert to and from the pandas dataFrame.

Parse unstructured or semi-structured data.

Apply prebuilt rules to analyze the data.

On the

General

tab, you can perform the following operations, and click

Submit

This section displays operations such as read, write, parser, and Pandas DataFrame functions that you can apply to your data.

To read from or to write data, specify the data source, connection, and the data object name.

To convert to or from the Pandas DataFrame, specify a variable name for the Pandas DataFrame.

To apply the parser function on unstructured or semi-structured data, provide a name for the data source, and specify the paths to the sample schema and the input file for the data to which you want to apply the parser function.

On the

Prebuilt Rules

tab, select the required pre-built rule to apply to your data, perform the following tasks, and click

Submit

This section displays functions such as prebuilt rules and the parser function that you can apply to your data.

Enter the variable name for the source.

Specify the applicable column name for the field based on the rule you select.

That's it! When you run the code, INFACore performs the configured operations on the data. If you want to check your activity, you can see it on the

Activity Log

page.

To configure any of these operations, you can also directly invoke the INFACore Python SDK. For more information about configuring these operations using the INFACore SDK for python, see the "Read and write end-to-end example" in the "Quickstart" section in the

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Table of Contents

JupyterLab Extension for INFACore

JupyterLab Extension for INFACore

End-to-end quick tour

End-to-end quick tour

Step 1. Set up the runtime environment

Step 2. Connect to the data source

Step 3. Explore the data