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 enables analysts, data specialists, and other domain experts to quickly cleanse and transform datasets of varying sizes for use in other analytics systems. 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 or 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

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1. D s item
   item account

   : You have a 

   D s item
   item account

    and can login. 
   1. If you do not have an account, you may be able to self-register through the application. 

   2. If not, please contact your 

      D s item
      item administrator

      . 

      See Login.

2. If you are importing data from a location other than your local file system, you must have the appropriate role in your user account. See Import Basics. 
3. Example data: You should use a sample set of data during this workflow.

 

Basic Workflow

1. Review object overview: Before you begin, you should review the overview of the objects that are created and maintained in

   D s product

   . See Object Overview.
2. Import data: Integrate data from a variety of sources of data. See Import Basics.
3. 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.
4. 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.

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.

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