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  • Import from flat file 

  • 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 workflows through the

d-s-webappproduct
.

Pre-requisites

Before you begin, please verify the following:

  1. D s item
    itemaccount
    :
    You  You have a
    D s item
    itemaccount
     and can login. 
    1. If you do not have an account, you may be able to self-register through the
      D s webapp
      application
  2. Example data: You  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 the 
    d-s-
    webapp
    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. Generate Results: Launch a task to run your recipe on the full dataset. Review results and iterate as needed. See Generating Results Basics.

  6. Export results: Export the generated results data for use outside of
    D s product
    . See Export Basics.

If you walked through this workflow in the

D s webapp
, you have imported, cleansed, transformed, and possibly enhanced your data for use in the next step of your analytics pipeline. Hopefully, this process has given you insight into the easy-to-use tools at your disposal through the
D s webapp
 and how quickly they can be brought to use in turning imported datasets into clean and actionable data for use across the enterprise.