Ingestion module for foundational DP

[Aashutosh-cognite]

Adds the Layer 2 orchestration module for dp:foundation. This module owns the ingestion workflow, all transformation definitions, and the auth groups needed to run them.

The module implements a two-phase workflow:

• Phase 1 (Population) — transformation tasks for PI, OPC-UA, and SAP run in parallel, landing data into the ISA Manufacturing Extension DM views (ISATimeSeries, ISAAsset, Equipment, WorkOrder, Operation).
• Phase 2 (Contextualization) — relationship transforms run after population completes, setting Equipment.asset and Operation.workOrder properties.

Which phases and tasks are included is driven entirely by config flags (enabledSources, enabledContextualization, dataModelVariant) — no YAML editing required when toggling a source on or off.

Key files:

• scripts/build_workflow.py — generates wf_ingestion_v1.WorkflowVersion.yaml from per-task snippets based on the active config. Run with --check in CI to detect drift.
• transformations/ — 8 scaffold SQL + YAML pairs (population: PI, OPC-UA, SAP assets/equipment/orders/operations; contextualization: equipment-to-asset, operation-to-order). SQL follows naming convention tr_{source}_{location}_to_{target}.
• auth/ — two self-contained groups: workflow service account (execute + transform) and workflow user (read-only monitoring).

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[Aashutosh-cognite]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces the cdf_ingestion_foundation module, which provides a framework for orchestrating two-phase ingestion workflows (population and contextualization) across PI, OPC-UA, and SAP source systems. It includes a Python generator script to build workflow versions from task snippets, various SQL transformations, and authorization group definitions. Review feedback primarily addresses Python style guide violations in the build script—such as import sorting, the need for typed data structures (dataclasses/Pydantic), and proper logging—as well as security recommendations to restrict overly broad wildcard scopes in the authorization group capabilities.

[gemini-code-assist]

Sort standard library imports alphabetically.

Suggested change

	import argparse
	import sys
	from pathlib import Path
	import argparse
	from pathlib import Path
	import sys

References

1. Sort alphabetically within groups. ^(link)

[gemini-code-assist]

Use dataclasses or Pydantic models to represent the configuration instead of untyped dictionaries. This ensures type safety and adheres to the project's coding standards.

References

1. Prefer dataclasses or Pydantic models over untyped dictionaries. Always parse file content into typed structures. ^(link)

[gemini-code-assist]

Use the logging module instead of print for warnings and informational messages.

References

1. Use the logging module with appropriate levels. ^(link)

[gemini-code-assist]

Missing docstring for load_tasks. Functions must have concise docstrings using the Args/Returns format.

Suggested change

	def load_tasks(filenames: list[str]) -> list[dict]:
	def load_tasks(filenames: list[str]) -> list[dict]:
	"""
	Load task snippets from YAML files.
	Args:
	filenames: list of task snippet filenames.
	Returns:
	list of task definitions.
	"""

References

1. Use concise docstrings with Args/Returns format. ^(link)

[gemini-code-assist]

The transformationsAcl uses a wildcard scope (all: {}). This is overly broad for a service account. Restrict the scope to specific datasets or IDs where possible.

References

1. Flag overly broad capabilities (e.g. wildcards '*' for dataSetScope, idScope, ACLs) in group definitions unless clearly justified. ^(link)

[gemini-code-assist]

The datasetsAcl uses a wildcard scope (all: {}). Consider restricting this to the specific datasets required by the workflow.

References

1. Flag overly broad capabilities (e.g. wildcards '*' for dataSetScope, idScope, ACLs) in group definitions unless clearly justified. ^(link)