Addressing Value-Level Issues


Next Steps
Overview Aggregate Cross-Calculate Custom Transforms Formats/Keys Lookups Match/Join Pivot/Unpivot Re-Map/Reformat Scrub/Cleanse Select/Filter Sort/Merge Substrings Type-Convert


There are many lower-level, data-value-related problems that ETL architects face, such as:

Issue Examples or Details
Applying format masks to represent or re-interpret values

ID'ing gender, 'F' or '2' or masking dates: mm/dd/yy to

Homogenizing inconsistent production keys
assigning unique numeric surrogates for recognition and faster indexing
Pivoting and unpivoting
transposing columns to rows and vice versa without duplicates
Inserting constants or literal strings
replacing or adding substitute values to protect or pre/re-label data
Setting or labeling values
evaluating a condition to determine a default label like 'NULL'
Validating values
determining if the inbound field is printable, or of a certain data type
Re-casting values to conform to reference standards
unifying different data representations (like country names)
Slowly changing dimensions in source tables
propagating new values (like a price change) in warehouse tables


The IRI Voracity data management platform and the IRI CoSort package both address all the above data handling issues, plus:

  • other data transformations
  • data type and format conversions
  • basic and advanced report generation
  • PII masking and custom test data synthesis

using a single 4GL data manipulation program called SortCL, which is also supported in their Eclipse GUI called IRI Workbench.

With SortCL, you can:

  • Remap dates and other data formats using new composite values, and re-usable data types
  • Re-assign source key names to adhere to new ontologies
  • Pivot (denormalize) for efficient queries, and unpivot (normalize) before loading data to the warehouse
  • Use in-line 'data' statements to add strings to any location, and with custom repetition counts
  • Use conditional data or field logic to label qualifying values, like inserts vs. deletes vs. updates, etc.
  • Use supplied data validation functions or third-party libraries to assess data characteristics or quality
  • Use integrated Perl Compatible Regular Expression (PCRE) logic to match and re-assert data patterns
  • Handle and report on multiple types of slowly changing dimensions and other changed data

IRI software like CoSort and RowGen (test data generation) help you generate unique initial or surrogate primary keys with features like:

  1. SEQUENCER, a specially named index field with custom starting and increment values
  2. random data generation, to create random numbers or strings, which can also be sorted and de-duplicated
  3. ROWID, a field with any name but specified as this attribute with features beyond SEQUENCER
  4. UUID (and GUID), a field-level transformation/generation function that can be used in any part of a job

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