How Can Organizations Architect a Scalable Data Engineering Pipeline to Support Real-Time AI-Driven

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How Can Organizations Architect a Scalable Data Engineering Pipeline to Support Real-Time AI-Driven Decision Making Across Multiple Domains?

Hello everyone,

I’m working on designing a data engineering pipeline that can support real-time AI analytics across multiple business domains, including marketing, operations, and supply chain. I want to ensure that our architecture is scalable, resilient, and compliant with data privacy regulations.

Specifically, I’m looking for insights on:

Data Ingestion & Streaming: What are the best practices for ingesting large-scale structured and unstructured data in near real-time? Which tools or frameworks are most effective for multi-source streaming pipelines?

Data Storage & Modeling: How can we efficiently store both batch and streaming data while maintaining a single source of truth for AI models? Are there recommended data lake or warehouse strategies?

Feature Engineering for AI: How can feature pipelines be automated and monitored to ensure high-quality inputs for machine learning models without introducing bias?

Real-Time Model Deployment: What architecture patterns (e.g., event-driven, micro-batching, or hybrid) best support real-time AI inference while minimizing latency?

Governance & Compliance: How do organizations ensure data privacy, lineage, and regulatory compliance while enabling agile AI experimentation?

Has anyone implemented a production-ready solution that addresses these challenges? I’d love to hear about architectural choices, tool recommendations, pitfalls, and lessons learned from real-world implementations.