File Preload Validation – ML-Powered Pre-Ingestion Validation

Overview: File Preload Validation Framework is an ML-powered pre-ingestion validation framework that auto-validates files and detects anomalies before data enters the pipeline. Reactive maintenance — where issues surface only after failures — creates operational drag. This framework shifts validation left, catching schema drift, anomalies, and format issues at the gate. Part of Coforge Data Cosmos™ – the innovation backbone comprising platforms, agents, and services that accelerates execution across every phase of the data lifecycle.

Challenges Addressed: • Reactive Maintenance — Issues detected only after pipeline failures • Schema Drift — Upstream changes break ingestion without warning • Data Spikes — Unexpected volume changes indicate source errors • Format Inconsistencies — Mixed formats cause silent corruption • Manual Validation — Hours spent manually checking files

Core Capabilities:

1. Automated File Structure Validation Validates structure against templates — column names, types, order, delimiter, encoding. Flags deviations before ingestion.

2. Schema Drift Detection Compares incoming schemas against baselines. Detects added/removed columns, type changes. Alerts operators and optionally blocks ingestion.

3. Volume & Pattern Anomaly Detection ML models learn historical patterns and flag outliers. Detects spikes, missing files, truncated loads.

4. Data Spike Identification Flags unusual value distributions, null changes, cardinality shifts.

5. Format Consistency Checks Validates CSV, Parquet, JSON, XML, fixed-width. Checks encoding and compression.

6. Pre-Ingestion Health Scoring Composite health score per file. Configurable pass/fail gates.

Industry Applications: • Banking — Validate regulatory feeds (BCBS 239, CCAR) before risk DW loading. • Insurance — Pre-validate claims batch files from TPAs. Volume anomaly flags missing files. • Travel — Validate GDS booking feeds for format and data spikes. • Healthcare — Pre-validate HL7/FHIR clinical data. Volume monitoring detects missing batches.

Business Benefits: • Eliminates reactive maintenance — catch issues before failures • Reduces downstream incidents by 40–60% • Automated validation replaces hours of manual checking • Configurable health scoring with pass/fail gates • ML-powered anomaly detection learns and adapts

Cloud-Native Deployment on AWS: Deployed on Amazon EKS. Amazon SageMaker hosts ML models. AWS Lambda for serverless triggers. Amazon S3 stores files and reports.