Exploratory Profiling
Profile bronze tables with pandas, visualize distributions in Lab, and decide what to promote to silver before modeling.
Profile bronze tables with pandas, visualize distributions in Lab, and decide what to promote to silver before modeling.
Business problem
New bronze ingests often arrive with unexpected nulls, skewed distributions, or schema quirks. Promoting bad data to silver wastes curation time and breaks downstream models.
Heimdall solution
Heimdall Lab runs Python notebooks directly on Lake bronze and silver tables. Analysts profile columns, plot distributions, and document findings before defining silver views or gold publish criteria.
Business impact
Data quality issues surface in Lab instead of in production forecasts. Teams promote only datasets they have visually validated.
How it works
Open bronze in Lab
Load catalog tables with pandas — no download to a local machine.
Profile and visualize
Use matplotlib, seaborn, or plotly to inspect distributions, outliers, and joins.
Promote with confidence
Define silver views or save Lab outputs knowing what the data looks like.
See Heimdall in action
Describe the datasets you need to profile
Key benefits
In-catalog exploration
Query Lake tables without leaving Heimdall
Visual validation
Charts inline in the notebook
Safer promotion
Fewer silver mistakes from unseen bronze issues
Analyst-friendly
Standard pandas workflows on governed data
Sandboxed execution
Read-only until you explicitly save results
Faster iteration
Re-run cells as new bronze batches arrive
Related use cases
More scenarios on the use cases hub.
Multi-Source Consolidation
Unify CSV exports, Postgres tables, and warehouse snapshots into one bronze catalog — no ETL scripts to maintain.
Unstructured Archive Ingest
Drop zip archives of PDFs, images, and documents into bronze storage for downstream structuring and ML pipelines.
Gold Feature Publishing
Curate silver views, publish gold datasets, and connect them to ML and Forecast training jobs.