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Case Studies chevron_right SchemaManager
schema DataOps · Data Quality

SchemaManager Schema Normalization

SchemaManager converts messy spreadsheets into predictable, reusable outputs. Users upload Excel or CSV files, define the exact target schema they want, and save it as a reusable template. On every new upload, the system generates the same standardized output. When filenames change or columns drift, AI resolves the mismatch by suggesting and applying the correct mappings, keeping downstream reporting and automations reliable.

1
Schema setup
100%
Repeatable outputs
Auto
Drift mapping
#DataOps #ETL #DataQuality #AI #Excel #CSV
Project Snapshot
Industry
Data operations
Use case
Schema standardization, data ingestion, and drift resistant transformation
Data
Excel and CSV uploads. User defined target schema. Historical mapping rules
Delivery
Web app workflow with saved schemas and repeatable export pipelines
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The Challenge

Spreadsheet-based workflows break constantly because input files are inconsistent and humans rely on fragile manual cleanup.

  • Different teams export the “same” report with different column names, ordering, and formatting.
  • Filenames drift over time, breaking automated ingestion and routing rules.
  • Traditional ETL requires engineering effort for each new source and every variation.
  • Without schema enforcement, reporting becomes slow, error-prone, and hard to audit.

The Solution

SchemaManager lets users define the target schema once, then enforces it automatically on every upload.

  • Upload: accept Excel and CSV files with minimal friction.
  • Define schema: users specify the output structure (fields, types, naming, order) and save it as a reusable template.
  • Repeatable transformation: each new file is transformed into the same output format without rebuilding rules.
  • AI drift handling: detect filename drift and column naming drift, then auto-map inputs to the saved schema using semantic matching and historical behavior.
  • Export: generate clean outputs ready for BI tools, databases, or downstream automation.

The Result

SchemaManager turns chaotic file ingestion into a stable, scalable workflow.

  • Less manual cleanup: users stop rewriting spreadsheets or creating one-off scripts.
  • Fewer broken pipelines: drift detection prevents silent failures and missing fields.
  • Faster onboarding: non-technical teams can standardize new data sources by defining a schema once.
  • More reliable reporting: consistent outputs improve auditability and reduce reconciliation time.