D toc

Excerpt
Learn the basics of how to import, wrangle, execute jobs, profile, and export your data from D s product r true .

Overview

D s product
r true

 enables analysts, data specialists, and other domain experts to quickly cleanse and transform datasets of varying sizes for use throughout the enterprise. Using an innovative set of web-based tools, you can import complex datasets and wrangle them for use in virtually any target system. Key capabilities include:

• Import from flat file, databases, or distributed storage systems

• Locate and remove or modify missing or mismatched data 
• Unnest complex data structures
• Identify statistical outliers in your data for review and management
• Perform lookups from one dataset into another reference dataset
• Aggregate columnar data using a variety of aggregation functions
• Normalize column values for more consistent usage and statistical modeling
• Merge datasets with joins
• Append one dataset to another through union operations

Most of these operations can be executed with a few mouse clicks. This section provides a basic overview of common tasks through

D s product

.

Prerequisites

Before you begin, please verify the following:

• D s item
  item account

  : You have a 

  D s item
  item account

   and can login. 

• Example data: You should use a sample set of data during this task.

Basic Task

1. Import data: Integrate data from a variety of sources of data.
   

   Tip
   Tip: When you login for the first time, you can immediately upload a dataset to begin transforming it.

   
   See Import Basics.

2. Profile your data: Before, during, and after you transform your data, you can use the visual profiling tools to quickly analyze and make decisions about your data. See Profiling Basics.
3. Build transform recipes: Use the various views in the Transformer Page to build your transform recipes and preview the results on sampled data. See Transform Basics.

4. Sample your data: In 

   D s product

   , you create your recipes while working with a sample of your overall dataset. As needed, you can take new samples, which can provide new perspectives and enhance performance in complex flows. See Sampling Basics.

5. Run job: Launch a job to run your recipe on the full dataset. Review results and iterate as needed. See Running Job Basics.

6. Export results: Export the generated results data for use outside of

   D s product

   . See Export Basics.

Object overview: You should review the overview of the objects that are created and maintained in 

D s product

. See Application Asset Overview.