Schema Inference
Summary
What it is
Using LLMs to infer or suggest schemas from semi-structured data (JSON, CSV, nested formats) stored in S3.
Where it fits
Schema inference automates the tedious process of determining what fields, types, and structures exist in S3 data. It accelerates onboarding new datasets and proposes schema evolution changes for existing tables.
Misconceptions / Traps
- LLM-inferred schemas are suggestions, not ground truth. Always validate against actual data samples before applying to production tables.
- Sampling matters. Schema inference from a small sample may miss rare fields or variant types that appear in the full dataset.
Key Connections
depends_onGeneral-Purpose LLM — requires language understanding for schema analysissolvesSchema Evolution — automates schema change proposalsaugmentsApache Iceberg — can suggest schema changes for Iceberg tablesscoped_toLLM-Assisted Data Systems, Table Formats
Definition
What it is
Using LLMs to infer, suggest, or validate schemas from semi-structured data (JSON, CSV with inconsistent headers, nested formats) stored in S3.
Why it exists
Semi-structured data arriving in S3 often has no declared schema. Manually inspecting files to determine field names, types, and nesting is tedious and error-prone. LLMs can analyze sample data and propose schemas automatically.
Primary use cases
Schema suggestion for new S3 datasets, validation of inferred schemas against existing table definitions, automated schema evolution proposals.
Relationships
Outbound Relationships
depends_onsolvesaugmentsInbound Relationships
Resources
Databricks Auto Loader documentation for automated schema inference from S3-stored files, sampling up to 50 GB to detect column types and handle schema evolution.
Official AWS Glue documentation for schema inference from S3 data sources, covering automatic type detection, crawler-based discovery, and Glue Studio's Infer Schema feature.
Research paper on using LLMs for schema inference on tabular data repositories, inferring entity types, attributes, and relationships from column headers and cell values.