Event-Driven Architectures for Real-Time Analytics and AI
Explains how event streams, message queues, and real-time processing power both operational analytics and context-rich AI agents.
Explains how event streams, message queues, and real-time processing power both operational analytics and context-rich AI agents.
A blueprint for building platforms where lineage, metadata, and access control are first-class citizens rather than afterthoughts.
Shows how LangChain changes the role of data engineers by connecting data pipelines with LLMs, tools, and retrieval-augmented generation.
Argues that data engineering must embrace software engineering discipline—testing, CI/CD, observability, and design patterns—to stay effective in the age of AI.
Compares SCD implementations across SQL, Spark, Delta, and Fabric, with guidance on when to choose Type 1 vs Type 2 and how to keep models maintainable.
Implementing robust batch scoring in Fabric, including patterns for scheduling, monitoring, and integrating predictions into downstream systems.