Metadata-Driven Multi-Tenant Data Ingestion for Cloud-Native Pipelines

Abstract

In contemporary enterprises, the utilization of cloud-native data platforms is becoming increasingly popular, in order to process large data streams, high-speed data streams, and non-homogenous data streams, which are introduced by the multiple organizational tenants. Traditional ingestion pipelines are often fixed schema-based and are not easy to expand dynamically over tenants having varying data format, governance policies and quality requirements. In the current paper, we propose a Metadata-Driven Multi-Tenant Data Ingestion Framework that relies on a declarative metadata, dynamic schema binding, and accomplished orchestration to efficiently define, validate, enrichment put simply as well as routing of cloud-oriented pipelines by automation.

 The proposed architecture will suggest a centralized Metadata Control Plane that will regulate tenant ingestion policy, data contracts, quality rules and security configurations. Metadata templates perform onboarding of data sources enabling flexibility in real-time and zero-code onboarding. It is a containerized ingestion layer that is event driven and provides pipelines dynamically with metadata-bound connectors and policy engines. The introduction of machine learning models is made to locate schema drift, anomaly, ingestion failure and intelligent resource scale.

Metadata approach is far superior in onboarding time, ingestion reliability and scalability, compared to their non-dynamic counterparts. It is also scalable and encrypted with isolate mechanism that guarantees actual multi-tenancy, secure policy implementation and can be used with large organizations, SaaS companies and regulated industries.

The suggested solution offers a secure, smart, and scalable foundation of next generation cloud-native data platforms, enabling organization to mobilize different data flows rapidly, and assure governance, quality and performance at scale.