Introduction
Data Engineering is no longer just about building pipelines—it’s about building smart, adaptive systems that feed the hungry brains of Gen AI models.
What’s Evolving?
DataOps & MLOps Integration
Use of LLMs in ETL/ELT Automation
Real-Time Data Pipelines Using Apache Kafka & Flink
Feature Stores for Generative AI Models
Industry Insight
With every company becoming data-driven, the demand for skilled data engineers with AI-awareness has risen by 48% in the past year globally.
Conclusion
Whether you’re building for recommendation engines or fraud detection systems, understanding Gen AI’s data hunger is crucial. Start mastering Spark, Airflow, Kafka—and add some OpenAI or LangChain knowledge to your toolkit!