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Data Quality Profiler and Rules Engine

Provides the following:

  • Data Profilers for large volume data profiling in Spark
  • Assertion rule definitions and checking
  • Reference data loading and joining
  • Excel and CSV reference data parsing
  • JSON output enriched with data quality markers/profilers
  • Metrics and summary dataframe output
  • Dimensional tagging of profiler outputs (additional identifiers)
  • JSON flattener
  • JSON and CSV loader, extensible to other formats
  • Custom key pre-processor and custom parquet row reader functionality
  • Comprehensive built-in assertion rules modules, extensible
  • Built-in set of field-level profile masks
  • Compound assertion rule definition (i.e. a set of sub-rules must all pass)
  • Human-readable Data Quality and Assertion Rule Compliance report output

Repository Layout

Usage

Releases are being managed by 6point6 at: https://github.com/6point6/data-quality-profiler-and-rules-engine

Changes are pushed upstream to the UKHomeOffice repo at: https://github.com/UKHomeOffice/data-quality-profiler-and-rules-engine

To use the Data Profiler classes, add the following dependency to your build.sbt, where the library is published to Maven Central:

libraryDependencies += "io.github.6point6" %% "data-quality-profiler-and-rules-engine" % "1.1.0"

Authors

Feel free to contex the authors for help/assistance.

Dr Daniel A. Smith - [email protected] - @danielsmith-eu

Licence

Licensed under the MIT License. See LICENSE