Multi-Provider Data Integration Platform

Context

Data arrived from many external providers with different formats, update frequency and reliability.

Problem

Inconsistent schemas, silent provider-side failures and no versioning meant data broke invisibly and was discovered by its consequences.

Constraints

Isolation of workflows from provider instability, reproducibility, schema-change auditing.

Architecture

Provider adapters → normalization → versioning → a data mart for workflows. Each provider is hidden behind a single contract.

AI layer

Anomaly classification in incoming data and schema-mapping suggestions when schemas change.

Event model

Data arrival and change are ingest, validation and mart-rebuild events; with no nightly batch runs.

Integrations

Heterogeneous provider APIs reduced to one normalized contract that hides differences from consumers.

Automation flows

Ingest, validation, deduplication, versioning, alerting on anomalies and discrepancies.

Infrastructure

Ingest queues, idempotency, isolation of individual provider failures from the rest of the system.

Observability

Monitoring of completeness, freshness and quality per provider individually, not “on average”.

Results

Workflows get stable data despite unstable sources; breakage is caught before consequences.

Lessons

Integration is a contract and versioning, not a one-off data move; “fine on average” hides a specific provider’s problem.