the-hf-stack/dagster-hf-datasets-examples/sanitization_observability - Storage Bucket

Dataset Sanitization & Observability

Clean and validate a noisy web-crawl dataset while exposing quality metrics as structured metadata in the Dagster UI.

What this example shows

• Chaining @hf_dataset_asset with downstream @asset nodes for multi-step cleaning
• Filtering null/empty/short text rows using Dataset.filter()
• Deduplication via prefix hashing (first 500 chars → MD5)
• @asset_check for post-cleaning validation with ERROR and WARN severities
• A dedicated quality report asset that emits structured metadata visible in the asset catalog

Dataset

HuggingFaceFW/fineweb-edu (sample-10BT config) — a 10-billion-token sample of web-crawl documents scored for educational quality. Real-world noise (short stubs, near-duplicates, malformed entries) makes it well-suited for demonstrating sanitization pipelines.

Note: The sample-10BT config is large. For local development, stream a small slice first or set streaming=True on the asset and adjust the pipeline accordingly (see example 10 for the streaming pattern).

Asset graph

raw_fineweb_edu
      │
      ▼
filtered_fineweb_edu        (drop null / short rows)
      │
      ▼
deduplicated_fineweb_edu    (drop prefix-hash duplicates)
      │           │
      ▼           ▼
 [checks]   cleaning_quality_report

Key API

# Ingest via decorator — function body receives injected dataset
@hf_dataset_asset(path="HuggingFaceFW/fineweb-edu", config="sample-10BT", split="train")
def raw_fineweb_edu(context, dataset: Dataset) -> MaterializeResult: ...

# Downstream transformation — plain @asset, dataset flows via IO manager
@asset
def filtered_fineweb_edu(raw_fineweb_edu: Dataset) -> Dataset: ...

# Asset check — validates the cleaned output
@asset_check(asset=deduplicated_fineweb_edu)
def check_no_null_text(deduplicated_fineweb_edu: Dataset) -> AssetCheckResult: ...

Asset checks

Check	Severity	Condition
check_no_null_text	ERROR	Zero null/empty text rows after deduplication
check_retention_rate	WARN	Cleaned dataset retains ≥ 80% of raw rows

Storage layout

.dagster_hf_storage/
├── raw_fineweb_edu/
├── filtered_fineweb_edu/
└── deduplicated_fineweb_edu/

cleaning_quality_report returns a plain dict and is not persisted by the IO manager.

Metadata visible in the Dagster UI

Asset	Key	Description
raw_fineweb_edu	null_text_count	Null/empty text rows in raw data
raw_fineweb_edu	short_text_count	Rows with < 10 tokens
cleaning_quality_report	retention_pct	% of rows surviving the full pipeline
cleaning_quality_report	dropped_rows	Total rows removed

How to run

pip install dagster dagster-hf-datasets

cd dagster_hf_datasets_examples

dagster dev -m sanitization_observability.definitions

Materialize assets in order: raw_fineweb_edu → filtered_fineweb_edu → deduplicated_fineweb_edu → cleaning_quality_report. Then run asset checks from the Checks tab on deduplicated_fineweb_edu.