Telmai Academy
  • Data Quality and Observability Academy
  • Basics of Data Observability
  • Data Quality Indicators
    • Introduction - Indicators of Data Quality
    • Selecting Data Quality Indicators
    • Completeness
    • Uniqueness
    • Freshness
    • Validity
    • Accuracy
    • Consistency
    • Data Lineage
  • Advanced Topic: Implementing DQ indicators
    • Completeness
      • Built-in
      • User-Defined
  • Correctness
    • Categorical (Nominal or Ordinal)
    • Numerical (Discrete or Continuous)
    • Structured
    • Semi-Structured
    • Unstructured
    • Uncommon Types
    • Designated Values
  • Profiling data
    • Basics of profiling
    • Interactive Profiling
  • Monitoring data quality
  • Monitoring definitions
    • SLO
    • SLI
    • Policies
    • Setting up policies and alerting
  • Monitoring Sources
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  1. Correctness

Semi-Structured

Data of unstructured nature, but still having some high level patterns applicable to it. This semi-structured data has a loosely defined component structure. We will focus on the Semi-Structured data that are common between verticals. Examples: email, url, address, etc.

Telmai can offer:

Pattern analyzer to identify accepted patterns (only compressed sequence patterns work in these cases and only sometimes)

Frequency analyzer to identify placeholder or over-represented data

Regex built-in syntax validators for email, url

Validation services API or libraries for email, url (see appendix below)

Validation and normalization of Address via external API calls (see appendix below)

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Last updated 3 years ago

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