AI is reshaping data engineering from batch plumbing to adaptive, feedback-driven systems. The talk maps concrete points of impact across the pipeline: LLM-assisted ingestion and schema inference; automatic ELT code synthesis and transformation validation; contract-aware quality checks with anomaly and drift detection; lineage reconstruction and metadata enrichment; cost- and carbon-aware orchestration; and policy-as-code access controls. Case patterns highlight measurable outcomes—faster onboarding of new data sources, lower incident rates, and improved reliability of downstream analytics and ML
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً