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Data Dare data transformation L4 sticker: the findi360 mascot analysing Enterprise Data Transformation and Performance Lab.

DD-TRF-004

Enterprise Data Transformation and Performance Lab

Decide what each monthly partition adds that the export does not yet hold, which partitions drifted, and whether the pipeline reconciles at scale.

  • Data Transformation
  • L4 · PARQUET
  • ~6,179,937 rows
  • 18 tables
  • 60 questions

Get the data

v1.0 · 6 files

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  • Question Book PDF DD-TRF-004_Question_Book.pdf 1.3 MB · v1.0
  • Dataset Documentation / Data Dictionary XLSX DD-TRF-004_Data_Dictionary.xlsx 14.1 KB · v1.0
  • Business Introduction PDF DD-TRF-004_Business_Introduction.pdf 614.6 KB · v1.0
  • Relationship Diagram PDF DD-TRF-004_Relationship_Model.pdf 616.1 KB · v1.0
  • Raw fact data DD-TRF-004_FactDataPipelineEvents.parquet 139.3 MB · v1.0
  • Dimension / supporting data DD-TRF-004_Supporting_Tables.zip 16.3 MB · v1.0

Questions

Open one to discuss it
  1. Q022 Hard Regions driving the year-on-year change
  2. Q023 Hard Category standardisation bridge
  3. Q024 Hard Reconcile batch Partition_2025_01 to the export
  4. Q025 Hard Reconcile batch Partition_2025_02 to the export
  5. Q026 Hard Reconcile batch Partition_2025_03 to the export
  6. Q027 Hard Reconcile batch Partition_2025_04 to the export
  7. Q028 Hard Reconcile batch Partition_2025_05 to the export
  8. Q029 Hard Reconcile batch Partition_2025_06 to the export
  9. Q030 Hard Reconcile batch Partition_2025_07 to the export
  10. Q031 Hard Reconcile batch Partition_2025_08 to the export
  11. Q032 Hard Reconcile batch Partition_2025_09 to the export
  12. Q033 Hard Reconcile batch Partition_2025_10 to the export
  13. Q034 Hard Reconcile batch Partition_2025_11 to the export
  14. Q035 Hard Reconcile batch Partition_2025_12 to the export
  15. Q036 Hard Sales by customer tier as at transaction date
  16. Q037 Hard Rows breaking the exception rules
  17. Q038 Hard Monthly rates against a flat rate
  18. Q039 Hard Top ten products before and after cleaning

General discussion

Anything about this dataset that is not one question — loading the files, which tool, how you set it up.

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