fabric-data-cleaner

name	fabric-data-cleaner
description	Algorithm reference and self-contained PySpark notebook templates for cleaning Microsoft Fabric lakehouse tables. Covers profiling, duplicate detection, null analysis, type validation, statistics, IQR outlier detection, date format validation, Spanish DNI/NIE checksum validation, and email/phone format checks. Each notebook can be uploaded and run independently in Fabric.
compatibility	Requires the Fabric MCP Server VS Code extension (fabric.vscode-fabric-mcp-server) for workspace and table discovery. Notebooks run on Microsoft Fabric's PySpark environment.

Fabric Data Cleaner — Algorithm Reference

Self-contained PySpark .ipynb notebook templates for cleaning Microsoft Fabric lakehouse tables. Each notebook can be uploaded and run independently — configure {{TABLE_NAME}} and {{LAKEHOUSE_NAME}} in the first cell, run all cells to analyze, then optionally run the fix cell.

Column Classification Rules

Classify the table's columns to select which notebooks to use:

Category	Detection Rule	Which Notebook
Numeric	Type is int, long, float, double, decimal	type_validation.ipynb, statistics.ipynb, outliers.ipynb
Date/Timestamp	Type is date or timestamp, or column name contains date, fecha, time	date_validation.ipynb
DNI/Personal ID	Column name contains dni, nie, nif, documento, identidad, id_personal	dni_validation.ipynb
Email	Column name contains email, correo, mail	contact_validation.ipynb
Phone	Column name contains phone, tel, movil, telefono	contact_validation.ipynb
Any column	Always applicable	profiling.ipynb, duplicates.ipynb, nulls.ipynb

Gotchas

• Notebook chaining: Fix-cell notebooks read from {TABLE_NAME}_cleaned if it exists, otherwise from the original table. This means running multiple notebooks in sequence chains their fixes — each one builds on the previous. Profiling and statistics always read the original for baseline comparison.
• Lakehouse binding: Notebooks deployed via REST API must include lakehouse metadata in their JSON (metadata.dependencies.lakehouse) or spark.table() will fail. The deployment script handles this automatically when you pass the lakehouse_id argument. Without the binding, the notebook fails during session init (~30-50s) with no detail error — do NOT misdiagnose this as a capacity issue. The MCP server may report capacityId: null even when a capacity is assigned; ignore that field.
• Table references use just the table name, not lakehouse.table. In Fabric, when the default lakehouse is attached, spark.table("my_table") works. Using spark.table("my_lakehouse.my_table") fails with TABLE_OR_VIEW_NOT_FOUND. The LAKEHOUSE_NAME config variable is for display/logging only — never use it as a Spark schema qualifier.
• In Spanish-locale CSV exports, commas are decimal separators (1.234,56 not 1234.56). The type_validation notebook detects and fixes this, but if you skip it, numeric casts will silently produce nulls.
• The date 01/02/2024 means February 1st in Spain (dd/MM/yyyy), not January 2nd. Always default to dd/MM/yyyy unless the user specifies otherwise. The date_validation notebook auto-detects the best format.
• Whitespace-only strings (" ") pass isNotNull() checks in Spark. The nulls notebook catches these; without it, they silently survive filters.
• DNI validation requires the exact mod-23 lookup table TRWAGMYFPDXBNJZSQVHLCKE. Do not attempt to compute the letter from scratch — use the UDF in the notebook.
• NIE prefix replacement is X=0, Y=1, Z=2 — not their ASCII values. This is a common implementation error.
• Fabric notebooks use display() instead of df.show() for rich rendering. All templates use display().
• Fix cells write to {TABLE_NAME}_cleaned as Delta tables. The original table is never modified. All notebooks write to the same _cleaned table — run them in order so each builds on the previous result.
• Delta schema mismatch: Notebooks that add flag columns (outlier flags, DNI valid, email/phone valid) change the schema of the _cleaned table. All fix cells use .option("overwriteSchema", "true") to handle this. Without it, the second notebook in the chain would fail with _LEGACY_ERROR_TEMP_DELTA_0007.

Notebooks — Always Applicable

1. Data Profiling — profiling.ipynb

Run first on any table. Shows schema, sample rows, and per-column null/distinct counts.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}

2. Duplicate Detection — duplicates.ipynb

Finds exact duplicates (window function over all columns) and near-duplicates (same natural key, different values). Fix cell removes exact duplicates via dropDuplicates() → writes {TABLE_NAME}_cleaned.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{KEY_COLUMNS}} (Python list, e.g., ["id", "fecha"]; use [] to skip near-duplicate check)

3. Null & Missing Value Analysis — nulls.ipynb

Counts NULL, empty string "", and whitespace-only values per column. Flags columns with >5% missing. Fix cell trims whitespace and replaces empties with NULL → writes {TABLE_NAME}_cleaned.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}

Notebooks — Numeric Columns

4. Type Validation — type_validation.ipynb

Detects non-numeric strings in columns that should be numeric. Also catches comma decimal separators (Spanish locale: ^\d+,\d+$). Fix cell replaces commas with dots and casts to DoubleType → writes {TABLE_NAME}_cleaned.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{NUMERIC_COLUMNS}} (Python list, e.g., ["precio", "cantidad"])

5. Statistical Summary — statistics.ipynb

Computes count, min, max, mean, median (percentile_approx), and stddev per numeric column. Read-only — no fix cell.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{NUMERIC_COLUMNS}}

6. Outlier Detection — outliers.ipynb

IQR method: bounds are [Q1 - k×IQR, Q3 + k×IQR]. Default k=1.5 (standard), use 3.0 for extreme-only. Fix cell adds boolean {column}_outlier_flag (does NOT delete) → writes {TABLE_NAME}_cleaned.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{NUMERIC_COLUMNS}}, {{IQR_MULTIPLIER}} (default 1.5)

Notebooks — Date Columns

7. Date Format Validation — date_validation.ipynb

For typed dates: checks for suspicious ranges (before 1900, future dates). For string dates: tries 8 common formats starting with the expected one, auto-detects the best match. Fix cell parses using the detected format → writes {TABLE_NAME}_cleaned.

Formats tested: yyyy-MM-dd, dd/MM/yyyy, dd-MM-yyyy, yyyy-MM-dd HH:mm:ss, dd/MM/yyyy HH:mm:ss, d/M/yyyy, yyyy/MM/dd, yyyyMMdd

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{DATE_COLUMNS}} (Python list), {{EXPECTED_DATE_FORMAT}} (default "dd/MM/yyyy")

Notebooks — Personal Data (Spanish)

8. DNI/NIE Validation — dni_validation.ipynb

Validates Spanish national IDs using the mod-23 checksum algorithm:

• DNI (8 digits + letter): number % 23 → lookup in TRWAGMYFPDXBNJZSQVHLCKE
• NIE (X/Y/Z + 7 digits + letter): replace prefix (X=0, Y=1, Z=2), then same algorithm
• Pre-processing: uppercase, strip spaces/dashes/dots

UDF returns (is_valid, reason) with reasons: valid_dni, valid_nie, empty, invalid_format, dni_bad_letter_expected_X, nie_bad_letter_expected_X. Fix cell adds {column}_valid flag → writes {TABLE_NAME}_cleaned.

See ./references/dni-validation-rules.md for the full algorithm reference.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{DNI_COLUMNS}} (Python list, e.g., ["dni", "documento_identidad"])

9. Contact Validation — contact_validation.ipynb

• Email: regex ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
• Spanish phone: 9 digits starting with 6/7/8/9, optionally prefixed with +34. Pre-processing strips spaces, dashes, dots, parentheses.

Fix cell adds {column}_valid_email / {column}_valid_phone flags → writes {TABLE_NAME}_cleaned.

Placeholders: {{TABLE_NAME}}, {{LAKEHOUSE_NAME}}, {{EMAIL_COLUMNS}} (use [] if none), {{PHONE_COLUMNS}} (use [] if none)

Notebook Deployment, Execution & Cleanup

Use ./scripts/fabric_notebook.py to manage notebooks in Fabric via the REST API. The script uses DefaultAzureCredential — it picks up the logged-in user's Azure CLI session (az login), so no .env or secrets file is needed.

Install dependencies (one-time):

pip install azure-identity requests

Deploy a notebook

Reads a local .ipynb file, injects Fabric lakehouse metadata (kernel info + default lakehouse binding), base64-encodes it with a .platform file, and creates a Notebook item via POST /v1/workspaces/{id}/notebooks. The lakehouse_id is required — without it, the notebook's Spark session has no lakehouse context and spark.table() calls fail.

python scripts/fabric_notebook.py deploy <workspace_id> <lakehouse_id> <notebook_name> <path_to.ipynb>

Returns the notebook ID on success. If the API returns 202 (provisioning), the script polls the operation URL until completion.

Run a notebook

Triggers an on-demand execution via POST /v1/workspaces/{id}/items/{notebook_id}/jobs/RunNotebook/instances. The notebook runs on the Fabric Spark cluster attached to the workspace.

python scripts/fabric_notebook.py run <workspace_id> <notebook_id>

Returns the job instance ID. The notebook runs asynchronously — use status to poll.

Check job status

Polls the job instance to check if the run completed, failed, or is still running.

python scripts/fabric_notebook.py status <workspace_id> <notebook_id> <job_instance_id>

List notebooks

Lists all notebooks in the workspace, or checks if a specific notebook exists by display name. Returns the notebook ID if found.

python scripts/fabric_notebook.py list <workspace_id>                # list all
python scripts/fabric_notebook.py list <workspace_id> <notebook_name> # find by name

Use list to check if a notebook already exists before deploying, or to retrieve the notebook ID after a 202 deploy.

Delete a notebook

Removes the notebook from the workspace via DELETE /v1/workspaces/{id}/notebooks/{notebook_id}. Use this to clean up after the cleaning notebooks have been run.

python scripts/fabric_notebook.py delete <workspace_id> <notebook_id>

Deployment gotchas

• Lakehouse binding is mandatory. The deploy command requires lakehouse_id because it injects metadata.dependencies.lakehouse into the notebook JSON and includes a .platform file. Without this, the Spark session starts with no lakehouse context and spark.table() fails immediately. The lakehouse ID can be obtained from Phase 1 discovery via MCP onelake_list_items.
• The create API may return 202 instead of 201 for notebooks with definitions. Always handle both — the script does this automatically.
• Notebook displayName must be unique within the workspace. If a notebook with the same name exists, the API returns ItemDisplayNameAlreadyInUse. Delete the old one first or use a different name.
• The RunNotebook job type is the correct one for notebooks. Do not use DefaultJob — that's for other item types.
• Job status polling: the Location header from the run response is the poll URL. Respect the Retry-After header (usually 30-60s).
• DefaultAzureCredential tries Azure CLI first, then managed identity, then environment variables. If az login is active, it just works.
• Idempotency — do not call deploy twice for the same notebook. The deploy command is not idempotent. If a deploy returns 202 (provisioning), wait for the operation to complete — do NOT call deploy again with the same name. The second call will fail with ItemDisplayNameAlreadyInUse. Similarly, after deleting a notebook, the name may take up to 90 seconds to become available; the API returns ItemDisplayNameNotAvailableYet (409, retriable) during this window. The correct pattern is: deploy → wait for completion → list to get the ID → run. Never fire-and-forget a deploy and immediately retry.
• One operation at a time. Do not deploy multiple notebooks in parallel — the Fabric API may throttle or return intermittent errors. Deploy, confirm success, then deploy the next one sequentially.
• Use list to verify state. Before deploying, run list <workspace_id> <notebook_name> to check if the notebook already exists. After deploying, use list to confirm it was created and retrieve its ID. This avoids duplicate deploys and guessing IDs.

Notebook Validation

Use ./scripts/validate_notebooks.py to verify generated notebooks before deployment. Run it against the output directory after generating customized notebooks.

python scripts/validate_notebooks.py <directory>

The script checks every .ipynb file in the directory for:

1. Valid JSON: json.load() succeeds without exceptions.
2. No remaining placeholders: no {{...}} strings anywhere in the file.
3. Notebook structure: top-level cells and metadata keys exist; every cell has cell_type and source; every code cell has outputs.

Exit code 0 means all notebooks passed. Exit code 1 means at least one check failed — fix the notebook and re-run. Exit code 2 means invalid usage (bad directory or no .ipynb files found).

Always validate before deploying. A notebook with broken JSON will fail to upload; leftover placeholders will cause runtime NameErrors in Spark.

Global Rules

• PySpark only. Use pyspark.sql.functions, never pandas for processing.
• Non-destructive. Fix cells write to a new {TABLE_NAME}_cleaned table. The original is never modified. All notebooks target the same _cleaned suffix.
• Fix cells require manual execution. Every fix cell is preceded by a markdown warning.
• Self-contained. Each notebook has its own imports, config, SparkSession, and table loading. No cross-notebook dependencies.