Often data from source systems requires preparation and data transformation prior to being loaded to another system. The Workiva Platform provides a number of transformation capabilities through both Chains and Data Prep.
This Connected Learning Path (CLP) interactively focuses on Chains various technical and lightweight functional transformation features. For more advanced transformation, we strongly recommend the use of Data Prep. Data Prep is an incredibly powerful application that elevates the functional transformation (mapping) capabilities of the Workiva Platform.
This transformation Connected Learning Path will walk you through setting up Chains to perform various valuable functions to demonstrate how data can be transformed by Chains while being transferred from a data source to a target. Several simple datasets have been provided based on common use cases. These examples are able to be used without regard to the various technologies that Chains supports.
Here is a list of paths and approximate time it might take complete a path. The goal of these paths is get you started on the concepts on light transformations within a Chain. The solution for each of these paths exist as Chain Templates in the Templates section of the Chains module under the Connected Learning Path folder.
| Exercise | Summary | Time (min.) |
| Configure Connections | Complete the setup tasks needed to complete this Connected Learning Path. This is a Pre-Requisite for starting the below defined paths. | 15 |
| Variable Transformation | Learn Variable Transformation date operations for parsing, formatting, and math operations. We also learn about the very powerful Handlebars Connector and Runtime Inputs as well as introduce JSON data. | 15 |
| Tabular Data | Learn some of the commonly used Tabular Transformation Connector Commands including Maps Headers, Unpivot, Find & Replace, Smart Filter, and Insert Column to modify tabular datasets. | 30 |
| XML Data | Learn how to process XML datasets, including applying filters while transforming. We also explore how to compare different datasets and identify differences. | 30 |
| Simple JSON Data | Learn how to process a simple JSON dataset to tabular, including applying filters while transforming. We also explore running simple queries and iteration. | 30 |
| JSON Nested Objects | Learn how to process more complex JSON data that contains nested objects. We also highlight a simple, yet powerful query that can be used to flatten a JSON dataset that contains nested objects. | 30 |
| JSON Array of Nested Objects | Learn how to process complex JSON data that contains an array of nested JSON objects. This exercise combines and reinforces a number of the concepts taught throughout this Quick Start including running queries, iteration, and Variable transformation. | 30 |
| Handlebars | Learn how Handlebars can be used to templatize variables in chains. This exercise illustrates how to leverage the handlebars Command to parse data from run time variables, output of a Workiva Command and a http response. | 30 |