CCC / PartsTrader
PartsTrader Catalog Ingest & Azure SQL Incremental Load Pipeline
Catalog ingestion pipeline that accepts CSV, TXT, XLSX, and related source formats, normalizes catalog fields, applies fitment-aware de-duplication, and publishes clean append-only records plus reporting-ready SQL views.
Catalog ingestion command view
PartsTrader Catalog Ingest Pipeline
Multi-format catalog intake, header normalization, quantity/MSRP cleanup, fitment-aware de-dupe, append-only SQL loading, and reporting views
Receive
CSV / TXT / XLSX
Normalize
Headers / Fields
Validate
Schema / Values
De-dupe
Fitment-aware
Publish
SQL Views
Catalog checks
File → SQLIncremental load preview
Append-only| Input | Cleanup | De-dupe | Output | Status |
|---|---|---|---|---|
| CSV catalog | Headers / Qty | Part + fitment | SQL rows | Loaded |
| TXT export | Delimiter map | Key compare | Staging | Validated |
| XLSX sheet | MSRP / fields | Existing keys | Append | Ready |
| Duplicate set | Review | Suppressed | Audit | Skipped |
SQL-ready outputs
Business problem
Catalog files arrived in multiple formats and needed clean loading into a reporting-ready SQL structure. Raw catalog inputs could vary by file type, header naming, quantity formatting, MSRP values, and fitment context.
The workflow needed a repeatable ingestion process that could handle mixed source files, clean the records, prevent duplicate loading, and publish SQL views that made the catalog usable for analytics and operational review.
System built
Built file ingestion for CSV, TXT, XLSX, and related formats with header normalization, part and manufacturer cleanup, quantity and MSRP cleanup, schema validation, fitment-aware de-duplication, append-only Azure SQL loading, and reporting-ready SQL views.
The system turns messy catalog files into a more reliable SQL-backed reference and reporting layer that can support inventory, procurement, and catalog review workflows.
Catalog controls
Signals reviewed
The loader evaluates file format, headers, part fields, quantity/MSRP quality, fitment context, duplicate risk, and publication readiness before records are appended to SQL.
Incremental catalog workflow
How it works
Receive
Accept catalog files across CSV, TXT, XLSX, and related source formats into one controlled ingestion flow.
The pipeline starts by making file intake flexible enough for real vendor and catalog data, where source formats are not always consistent.
Normalize
Standardize headers, part fields, manufacturer fields, quantity values, and pricing-related fields.
Normalization gives the catalog loader a stable internal shape before validation, de-dupe, and SQL loading occur.
Validate
Check required fields, schema readiness, quantity/MSRP quality, and fitment context before records are accepted.
Validation helps prevent bad catalog records from polluting the reporting layer and reduces cleanup work after loading.
De-dupe
Compare incoming records using fitment-aware logic so repeated catalog files do not duplicate records incorrectly.
The matching logic considers more than the part number alone, giving the loader better context when handling fitment-based catalog records.
Publish
Append clean records into Azure SQL and expose reporting-ready SQL views for downstream use.
The final layer turns messy catalog files into a durable data asset that can support analytics, inventory review, and operational lookup workflows.
System layers
What the catalog pipeline coordinates
File intake layer
Handles catalog source files in several formats so vendor/catalog data can enter the same controlled processing path.
Normalization layer
Cleans headers, part fields, quantities, MSRP values, and supporting catalog attributes before validation.
Fitment-aware de-dupe
Compares incoming records with existing catalog keys using part and fitment context to prevent duplicate loading.
SQL publication layer
Loads clean records into Azure SQL and exposes reporting-ready views for analytics and operational review.
Impact signals
What the pipeline improved
Multi-format catalog ingestion for CSV, TXT, and XLSX files
Header and field normalization before SQL loading
Quantity and MSRP cleanup for cleaner catalog records
Fitment-aware de-duplication to reduce duplicate record risk
Reporting-ready SQL views for downstream analysis
Operational value
Catalog files turned into a reusable SQL asset
More flexible catalog intake
Supports catalog files arriving in multiple formats without forcing every source into one manual template first.
Cleaner SQL records
Standardizes headers, values, and schema expectations before data reaches Azure SQL.
Better duplicate control
Uses fitment-aware comparison so duplicate prevention reflects how catalog data actually behaves.
Reusable reporting layer
Publishes clean records and SQL views that can support analytics, lookup tools, inventory review, and future automation.
Why this project matters
Catalog ingestion gets stronger when file flexibility, cleanup, de-duplication, and SQL publication are handled together.
This project shows how catalog files can move from inconsistent vendor-style exports into a structured SQL layer. Multi-format intake, normalization, quantity/MSRP cleanup, fitment-aware de-dupe, and reporting views make the data easier to trust and reuse.
The value is not only loading catalog data. The value is creating a dependable catalog foundation that can support analytics, inventory review, procurement logic, and future operational systems.
Confidentiality note
Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, catalog source files, customer records, internal pricing, Azure SQL credentials, operational screenshots, proprietary matching logic, or source pipeline files.