AI-ready data

Output is typed, schema-consistent Parquet and warehouse-native tables. Downstream ML, embeddings, and agent workloads consume the same tables you already query.

What you get

• Structured Parquet — columnar, compressed, typed.
• Schema consistency — every record follows a validated schema.
• Type reconciliation — when a field’s type changes, Skippr adds a new column instead of mutating in place.
• Natural-language to SQL — skippr ask translates questions into SQL against your warehouse tables.

Pipeline

1. Ingest — exactly-once delivery; unparseable records dead-lettered.
2. Structure — schema discovery and type mapping.
3. Output — Parquet / warehouse tables.
4. Consume — analytics, feature stores, RAG, and agents.

Row-level data stays on the path from the runner to your destination. See ELT home.

Related

• Data modeling
• skippr vector