JupyterLab Extension for INFACore

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Explore the data

You can apply functions on your data to help you analyze the content and structure of your data and transform your data based on your business requirements. You can improve the quality of your business data and create quality data sets.

After you connect to a data source, you can perform the following operations on your data:

Read from or write data to a data source end point.

Choose to apply the Pandas functions to your data to represent the data in a Pandas DataFrame.

If your data is converted to a Pandas DataFrame, you can convert it to the Informatica DataFrame before you write to the target.

The time that the to_pandas function takes to convert data to a Pandas DataFrame and for the from_pandas function to convert the data from the Pandas DataFrame to the Informatica DataFrame scales linearly with the size of the data that you are processing.

Apply parser transformations to parse unstructured or semi-structured data.

Apply a number of built-in rules to verify, standardize, and address issues in the source data.

The following image shows the

General

tab with the list of operations that you can perform on your data:

The following image shows the

Functions

tab with the list of data quality functions that you can apply on your data:

When you select a function and specify the data source and the column name for which you want to apply the function, the parameters are added to the code in your development environment and you can run the code to get the output you want.

Read data and convert to Pandas DataFrame

Convert to INFACore DataFrame and write data

Provision data

Parse unstructured data

Apply pre-built rules

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