Frequently Used Nodes and Functions in Data360 Analyze

In Data360 Analyze, the key buildings blocks for easily building your logic or your "data flow" are the nodes and functions that come pre-built into the product.

• A node is an individual piece of functionality that applies logic to the underlying data at that particular step in the data flow. 
• A function is a pre-built instruction that performs an operation and returns a value. 

This article directs you towards some commonly used nodes, functions and operators to help you get started. 

Commonly Used Nodes

By default, a small subset of the available nodes is displayed, which is ideal for new users who are just getting started creating data flows. This subset is called Favorites, and consists of a few of the most commonly used nodes. Substantial data flows can be created from just this subset. 

Hover over the node to see a brief description of what it does:

Choose All Nodes from the drop-down list to see all of the nodes which are available for creating your data flow.

Code-free Data Preparation

A range of common data preparation tasks can be performed in the Data Viewer without having to use any coding. You can sort and filter data using the context menu for a data field.

The filter options that are displayed depend on the field's data type. If you have a single cell selected in the data viewer when you create the filter, the cell's value will be populated in the displayed filter dialogue - you can edit this value if required.

When a Filter or Sort has been applied to the data, you can add it to the canvas as a pre-configured Filter/ Split or Sort node.

If required you can also manually add a Filter, Split or Sort node to the canvas and then use the grid editor to configure the node without having to write any code.

You can customize the operation of a Filter or Split node clicking the Advanced button, which allows you to view and edit the underlying Python code used by the node.

Even if you are not yet at the stage where you want to write your own custom filter expressions viewing the code can be a helpful learning exercise to familiarize you with using Python in Data360 Analyze.

Using the Python Language

Python has superseded the proprietary Scripting that was used by earlier versions of Data360 Analyze. The proprietary Script is still available within the product but Infogix recommends the use of the Python language for current projects.

There are many resources available online that can help familiarize you with the Python such as

https://docs.python.org/2/tutorial/.

The Data360 Analyze Help provides information on Data360 Analyze-specific functions and how we bind records and metadata, and details of the custom Python modules that are provided with Data360 Analyze. See the Python Scripting section of the embedded Help or the corresponding section in the online documentation here.

Examples

You can find node examples installed with your Data360 Analyze instance: 
<Install Directory>\docs\samples\nodes.

To view the examples in Data360 Analyze, please import the *.lna file by following the instructions found in Data360 Analyze Help > Advanced topics > Sharing documents