Intent
Extract data from source systems and export it as stable, reusable artifacts.
When to use
- You need a repeatable data export for reporting or integrations.
- Downstream systems rely on snapshots that must be consistent.
- You need a clear boundary between source systems and consumers.
Core mechanics
- Define the extraction query or API contract.
- Normalize data to a stable schema and format.
- Version and store exports with timestamps and metadata.
- Publish data dictionaries and refresh cadence.
Implementation checklist
- Document source systems and extraction queries.
- Define the export schema and data dictionary.
- Implement validation and row count checks.
- Schedule the extract and publish cadence.
- Store exports with retention rules and metadata.
Failure modes and mitigations
- Source schema drift -> detect and block unsafe changes.
- Partial extracts -> validate row counts and completeness.
- Stale exports -> surface freshness timestamps and alerts.
Observability and validation
- Extraction duration and error rate.
- Row count deltas between runs.
- Export freshness and last successful run time.
Artifacts
- Export schema and data dictionary.
- Sample exports and validation reports.
Related Atlas projects
- BOUSD-AeriesDataExportFormatter-Extract
- BOUSD-ClassSize-Extract
- BOUSD-DataConfDocs-Extract
- BOUSD-Enrollment-Extract
- BOUSD-MonthlyAttendance-Dashboard
- BOUSD-MonthlyAttendance-Extract
- BOUSD-PADC-Extract
- BOUSD-Staff-Technology-Dashboard
- CertManager
- PrivateGPTConf/env/default
- csv_mapper
- google-groups-guard
- gpt_kit
- informedk12-sync
- mssql_query_to_csv
- pages-aether
- privateGPT
- sql_to_csv
- titanhst_sync