ofwat-dataprepkit 0.14

ETL helpers focused on Fabric SQL workloads

dataprepkit metadata-driven SCD2 orchestrator

dataprepkit turns CSV snapshots into evolving dimensions by declaring the schema, keys, and dependency joins in metadata. The loader and scd2.apply_changes take care of schema evolution, dependency joins, normalizing datetimes, and the SCD2 workflow.

Metadata format

Each metadata entry registered via register_metadata(name, metadata) must include:

Key	Description
target_table	Table name (schemaless or schema-qualified; target_schema can fill in the schema).
target_schema	Optional schema name. The loader auto-prefixes it during registration and creates the schema if missing.
natural_key_cols	List of natural key columns—required.
natural_key_specs	Optional overrides (ColumnSpec) for natural keys (type, nullable, unique, default).
data_columns	Map of column names → ColumnSpec. Required.
surrogate_key / join_numeric_key	System column names.
filepath	CSV path (or lakehouse path) to read.
schema_handling.mode	"suggest" (default) or "evolve" (auto-add missing columns).
processing_class	Optional callable that transforms the incoming pandas DataFrame.
dependencies	Optional dependency definitions (see below).
run_policy	Determines failure handling (continue or abort).
archive_path	Optional location for parquet archives.

ColumnSpec fields:

• type: SQL type (e.g., NVARCHAR(4000), DATETIME2(3)).
• nullable: boolean.
• unique: boolean flag.
• default: SQL default expression.
• parse_format: optional format string for datetime parsing before staging.

Example:

{
  "schema_handling": {"mode": "evolve"},
  "target_table": "tbl_d_company",
  "target_schema": "Dimensions",
  "natural_key_cols": ["Organisation_Cd"],
  "data_columns": {
    "Legacy_Company_Name": {"type": "NVARCHAR(4000)", "nullable": true},
    "New_Column_From_Join": {"type": "NVARCHAR(4000)", "nullable": true}
  },
  "surrogate_key": "surrogate_key",
  "join_numeric_key": "join_numeric_key",
  "filepath": "/lakehouse/.../company_dim.csv",
  "dependencies": [
    {
      "schema": "Dimensions",
      "table": "tbl_d_company_service",
      "on": [{"source": "Service_Type_Cd", "target": "Service_Type_Cd"}],
      "select": {"Service_Description": "New_Column_From_Join"},
      "where": {"target": ["Current_Ind == 1"]},
      "how": "left",
      "on_missing": "null"
    }
  ]
}

Dependency joins

DependencyJoin entries support:

• schema / table: lookup table to join to.
• on: list of {source, target} mappings between incoming and lookup columns.
• select: mapping of lookup column → alias added to the incoming frame.
• where: optional SQL filters applied before the join.
• filter_target_current: restricts to Current_Ind = 1 rows (default True).
• how: "left" or "inner".
• on_missing: "null" or "error".

Joins run inside SQL; pandas only reads the filtered result set, renames columns, and maps the selected values to the incoming DataFrame.

Runtime flow

1. Register metadata (see examples/run_metadata_example_fabric.py).
2. Call run_dimension(engine, metadata_name, csv_reader=...).
3. Loader steps:
   • read CSV (custom reader may handle lakehouse authentication);
   • cast datetimes using optional parse_format;
   • ensure schema/table exist (auto-create/evolve if needed);
   • run SQL dependency joins;
   • call scd2.apply_changes with computed hashes and nullable columns.

Tests

• python -m pytest dataprepkit/tests/test_metadata_loader.py — metadata parsing, datetime casting, dependency joins, schema aliases.
• python -m pytest dataprepkit/tests/scd2/test_apply_changes.py::test_nullable_data_column_allows_null_staging — staging with nullable columns.

Examples

• examples/run_metadata_example_fabric.py – registers multiple metadata objects and drives Fabric-based loading.
• examples/run_scd2_fabric.py – demo insert/update/delete/reinsert phases on Fabric tables.

Notes

• Metadata entries are validated via Pydantic; missing keys raise errors.
• Update metadata before rerunning run_dimension to maintain the registry.
• The system is designed for declarative metadata so you can add dimensions by editing metadata, not code.

Helpers modules

dataprepkit.helpers exposes reusable utilities:

• helpers/connectors/fabric.py – Fabric SQL connection builder that handles MSI tokens, driver selection, and pooling. Use create_engine_for_fabric(endpoint, database, preferred_driver, ...) plus validate(engine) before running any metadata loads.
• helpers/storage.py – Lakehouse/mount helpers (mounting, paths) used by the Fabric examples; consult it for paths when you need to read raw CSVs stored in your lakehouse.
• Additional helper modules (e.g., connector-specific tooling) are referenced by the examples so you can replicate the Fabric patterns on other platforms.