The Database That Tells You What It Knows

Description and use

Atlas integrates storage, search, inspection, repair, and verification for semi‑structured data into a single embedded engine, providing a transparent audit trail and consistent data quality.

Problem

Semi‑structured data often arrive with missing fields, inconsistent formatting, and other quality issues that external scripts must detect, repair, and audit, leading to fragmented tooling and fragile pipelines.

Outcome

Atlas unifies storage, search, inspection, repair, and verification into a single, durable system, eliminating external tooling, reducing inconsistencies, and providing a transparent audit trail.

Workflow

• Ingest JSON/CSV/JSONL
• Store records in append‑only file with checksums and write‑ahead journal
• Maintain persisted inverted index for full‑text search
• Infer schema and profile data quality
• Detect and classify issues without modifying data
• Propose safe repairs and quarantine uncertain records
• Apply repairs with audit trail
• Verify storage, journal, index, and audit integrity

Evidence

Atlas is a zero‑dependency embedded database for semi‑structured data. It stores records, builds a full‑text search index, infers schema, analyzes data quality, proposes safe repairs, preserves uncertain records, and records an audit trail of applied changes.

Source

Atlas accepts JSON arrays, JSON Lines, and CSV files. CSV values receive conservative type coercion, and JSONL and CSV inputs can be processed incrementally.

Source

Atlas detects and classifies issues. It does not modify records during diagnosis or dry‑run repair.

Source