You can create your first job using INFACore in just a few steps!
When you log in to INFACore from JupyterLab, the INFACore Home page appears in JupyterLab, as shown in the following image:
On the left-hand side, you can see the JupyterLab work area where you can programmatically invoke the INFACore Python SDK. On the right-hand side, the JupyterLab interface for INFACore provides you with the options to perform basic operations to get you started.
You can perform the following tasks from the INFACore user interface:
1Set up the runtime environment.
Before you can connect to a data source or to apply functions to a data source, you need to install the runtime environment. The runtime environment is the execution platform for running all the INFACore jobs.
2Connect to your data source.
The data source is the endpoint from where you want to read from or write data. Select the data source to which you want to connect and then create a connection to connect to the data source.
3Apply functions to your data.
Functions represent the operations that you want to perform on the data.
To invoke the INFACore Python SDK for various operations, see the INFACore SDK Reference for Python.
After you create your first job, you can also:
•Monitor the activity of your jobs and operations in the Activity logs page.
•Download the log files for a job to the agent machine.
•Use the in-built forms to refer INFACore to colleagues, write a feedback about INFACore, or to submit requests. In the Connect to Data Sources section, you can request for new connections to access data source endpoints from INFACore. In the Apply Functions section, you can also submit a request for new functions, with the purpose defined.
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.
1In 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:
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.
1In 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.
2On the Connections tab, select an existing connection for the data source from the list, or create a new connection to the data source.
3To create a new connection, click the + icon, and then specify the details for the data source that you want to connect to.
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:
When you create and save a new connection, that connection displays in the connection list.
4Select the required connection and perform one of the following actions:
aTo 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.
bTo edit the connection, select the icon, edit the connection details, and save the connection.
cTo 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.
1On the General tab, you can perform the following operations, and click Submit:
aTo read from or to write data, specify the data source, connection, and the data object name.
bTo convert to or from the Pandas DataFrame, specify a variable name for the Pandas DataFrame.
cTo 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.
2On the Prebuilt Rules tab, select the required pre-built rule to apply to your data, perform the following tasks, and click Submit:
aEnter the variable name for the source.
bSpecify 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 INFACore SDK Reference for Python.
to install the agent on your machine.
icon, and provide a variable name for the connection to display in the Python code.
icon, edit the connection details, and save the connection.
icon.
