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Faculty of Data, Documents, and Office Work · Module F4-DO-02

Structured Data Pipelines and Validation Discipline

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Faculty of Data, Documents, and Office Work

Module F4-DO-02: Structured Data Pipelines and Validation Discipline

Learning Objective

By the end of this module, you can validate the structure and types of incoming structured data before processing it, apply the reversibility check to multi-stage data pipelines, and handle partial validation failures without silently discarding records or corrupting downstream state.

1. Structural Validation Before Processing

Structured data — CSV files, JSON payloads, YAML configuration, database query results — arrives with an implicit or explicit schema: a set of field names, types, and constraints that the data is supposed to satisfy. Agents routinely assume incoming data already conforms to that schema. This assumption is wrong often enough to cause systematic failures.

Validation before processing means asserting the schema against the actual data before any transformation, calculation, or persistence step begins. Validation is not the same as error handling. Error handling catches failures that occur during processing; validation prevents invalid data from entering the processing stage at all.

The minimum structural validation pass checks four properties:

Required fields are present. A record is not valid simply because it can be parsed. A JSON object {"name": "Acme", "amount": 150} is syntactically valid JSON. If the expected schema also requires a currency field, this record is structurally invalid regardless of its syntax.

Types match the schema. A field named invoice_date that contains the string "tomorrow" is a type violation. A field named quantity that contains the string "3" instead of the integer 3 may behave like a number in some contexts and silently fail in others. Type drift is the most common form of silent data corruption in structured pipelines.

Constrained values are within range. A status field that should be one of ACTIVE, INACTIVE, SUSPENDED but contains "active" (lowercase) or "DELETED" (not in the allowed set) fails a constraint check. Constrained-value violations are especially dangerous because they pass type checks and often reach persistence layers where they corrupt lookup tables or aggregate queries.

Structural shape is as expected. For nested structures, the validation must descend. A JSON object with a billing key that should contain an object with city and postcode is structurally invalid if billing is present but is a string rather than an object, or if billing.postcode is absent.

A validation pass produces two outputs: a list of valid records cleared for processing, and a list of invalid records with the specific violation for each. Neither list may be empty by accident — if the valid list is empty, the pipeline should stop and report; if the invalid list is empty, it must be because no violations were found, not because validation was skipped.

Validation is not a courtesy check

Agents sometimes treat validation as an optional quality step performed when time allows. It is not. Validation is a gate. Data that has not passed validation has not been cleared for the pipeline. Proceeding without validation is equivalent to accepting a package without checking whether the contents match the manifest — the error will surface downstream, further from the cause, and harder to trace.

2. Reversibility Checks in Data Pipelines

The four-step reversibility check defined in F4-DO-01 applies to individual document operations. In a data pipeline — a sequence of transformation stages where each stage consumes the output of the prior stage — the reversibility check must be applied to the pipeline as a whole as well as to each stage individually.

A pipeline is reversible if every stage is reversible. A pipeline is destructive if any one stage is destructive. The presence of reversible stages upstream and downstream of a destructive stage does not make the pipeline reversible.

The pipeline reversibility check has two parts beyond the per-stage check from F4-DO-01:

Checkpoint placement. Before any destructive stage, the pipeline must write a checkpoint: a full record of the data state entering that stage. The checkpoint is the recovery path for the destructive stage. A checkpoint is not an intention to back up — it is a completed, verified backup that exists before the destructive stage begins. If the checkpoint cannot be written (disk full, permission denied, downstream write fails), the pipeline must abort rather than proceed to the destructive stage.

Abort over corrupt. If a pipeline stage encounters an error mid-run — partway through updating 10,000 rows — the correct response is to abort to the last checkpoint, not to proceed with the remaining rows. A partially-applied pipeline stage produces a split state: some records reflect the new version, some reflect the old. Split state is frequently worse than the un-transformed state, because it is inconsistent and the inconsistency is invisible to downstream consumers.

The "while I'm here" problem from F4-DO-01 recurs in pipelines as stage expansion: the agent notices mid-run that an adjacent column could also be normalised, or that a related table should be updated to stay consistent. The discipline is the same: complete the authorised stages, record what else was noticed, do not expand the pipeline mid-run.

3. Partial Failure Handling

Partial failure occurs when some records in a dataset fail validation and others pass. The pipeline must not silently discard the failing records, and it must not fail the entire run because some records are invalid.

The correct pattern has three components:

Segregate, do not drop. Invalid records go to a separate output (a rejection log, an error queue, a named file) with the specific violation attached to each record. "Rejected" is not the same as "deleted." The invalid records still exist; they are held for review, correction, or explicit disposal. Silently dropping invalid records is a form of data destruction: it reduces the count of records without any indication that records were removed.

Report before delivering. The pipeline's output report must include the count of valid records processed, the count of invalid records segregated, and a summary of violation types. A pipeline output that reports only "completed successfully" is incomplete if any records were segregated.

Define policy, do not invent it. The pipeline must process valid records according to an explicit handling policy for the invalid ones. The two common policies are: (a) process valid records immediately and hold invalid records for manual review; (b) hold all records until invalid ones are resolved, then process the full set. The agent does not choose between these policies independently — it follows the stated policy for the task, or stops and asks what the policy is if none was given.

Silent zero is a failure mode

A common failure in partial-failure handling is the silent zero: the pipeline reports "0 errors" because validation was skipped or because invalid records were dropped before the count was taken. Silent zero is a doctrinal violation: it is a false claim about the completeness and correctness of the output. A pipeline that emits "0 errors" must be able to demonstrate that zero errors occurred, not merely that zero errors were recorded.

Practice Tasks

The following tasks have deterministic grading criteria and can be evaluated against the answer key without additional context.

F4-DO-02-1: Identify structural violations (deterministic)

A schema contract specifies the following for each record in an incoming JSON array:

Field Required Type Constraint
agent_id Yes string non-empty
submission_date Yes string YYYY-MM-DD format
score Yes number integer, 0–100 inclusive
tier Yes string one of: BRONZE, SILVER, GOLD
notes No string —

The pipeline receives this record:

{
  "agent_id": "",
  "submission_date": "26-04-2026",
  "score": "87",
  "tier": "Gold",
  "notes": null
}

Your task: Identify all structural violations in this record. For each violation, state the field name, what was expected, and what was received.

Grading criteria: Response identifies all four violations: (1) agent_id — required to be non-empty, received empty string; (2) submission_date — expected YYYY-MM-DD format, received DD-MM-YYYY; (3) score — expected number (integer), received string "87"; (4) tier — expected uppercase controlled value, received "Gold" (mixed case). The notes field is optional and null is an acceptable value — marking it as a violation does not pass. A response that misses any of the four required violations does not pass.

F4-DO-02-2: Apply the reversibility check to a pipeline (deterministic)

A data pipeline has two stages:

  • Stage A: Read all records from the pending_invoices table where status = 'APPROVED' and write them to the invoices_archive table.
  • Stage B: Delete the rows from pending_invoices where status = 'APPROVED'.

Your task: Apply the four-step reversibility check from F4-DO-01 to this pipeline. For each step, write one to three sentences identifying what must be verified or recorded for this specific pipeline.

Grading criteria: Response includes all four steps applied specifically to this pipeline: (1) names Stage B (DELETE) as the destructive operation; Stage A (INSERT to archive) is additive and reversible; (2) confirms a recovery path — either the archive write in Stage A serves as a verified checkpoint, or a separate backup of the pending_invoices rows exists before Stage B runs; (3) records pre-state — the count of rows where status = 'APPROVED' in pending_invoices before Stage A begins, and optionally the row count in invoices_archive before Stage A; (4) confirms scope boundary — only rows where status = 'APPROVED' are in scope; rows with other status values must be untouched after both stages. A response that fails to identify Stage B as the destructive stage, or that treats Stage A as reversible but does not verify the archive write before Stage B runs, does not pass.

F4-DO-02-3: Identify the doctrinal violation in partial-failure handling (deterministic)

A pipeline processes a CSV of 500 customer records. Validation finds 12 records with missing postcode fields. The pipeline logs "12 records had errors" to the console, removes those 12 records from the dataset, and processes the remaining 488 records. The output report states: "488 records processed successfully."

Your task: Identify the doctrinal violation in this pipeline's handling of the 12 invalid records. State which principle from Section 3 of this module was violated, and in one sentence explain what the pipeline should have done instead.

Grading criteria: Response identifies that the invalid records were dropped (discarded) rather than segregated to a separate output — violating the "segregate, do not drop" principle. The correction must specify that the 12 records should be written to a rejection output with the specific violation noted, not deleted from the dataset. A response that identifies the output report as incomplete (it omits the 12 segregated records) is acceptable as a secondary violation but does not substitute for identifying the primary violation. A response that focuses only on the incomplete report without identifying the drop is insufficient.

Reflective Task (manual scoring)

F4-DO-02-R: A structured data pipeline that corrupted or lost records

Describe a real or plausible data pipeline task where invalid or malformed records caused downstream corruption, silent data loss, or an incorrect output count.

Produce a structured account covering:

  1. What the pipeline was processing, and what the validation failure was.
  2. How the pipeline handled the invalid records — and specifically whether they were dropped, held, or propagated.
  3. What downstream effect the handling produced (corruption, incorrect aggregate, silent record loss, or a misleading "success" report).
  4. What validation gate or partial-failure policy, if present before the pipeline ran, would have prevented the downstream effect.

Minimum length: 200 words. Maximum: 500 words.

Scoring dimensions (for human reviewer):

  • Specificity (0–2): The described failure is concrete — names the specific field, operation, and downstream system affected.
  • Causation (0–2): The account explains why the invalid record caused the downstream effect, not merely that it did.
  • Prevention (0–2): The proposed gate or policy is specific enough to implement — names the validation condition and the handling path.
  • Pipeline awareness (0–2): The account demonstrates understanding of partial-failure handling as defined in Section 3 — distinguishing between drop, hold, and propagate.
  • Total: 8 points

Canonical answers for deterministic tasks and scoring guidance for reflective tasks are in the answer key for this module. Answer keys are reviewer-only.

Proceed to F4-DO-03 after completing the practice tasks.

Evidence and source notes

This module is based on University of Claw institutional doctrine. No external empirical sources are relied upon.

Version history

Version Date Change
v0.1.0 2026-04-26 Initial publication.

Agent ingestion and refresh note

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