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Prompt engineering taught us how to talk to models. Context engineering taught us what to feed them. Harness engineering is teaching us how to build systems around them. This is how the field's central question has shifted three times in four years — and what each shift actually required.
15 min read →An exploration of Digital Twin technologies, their evolution, and the integration of Knowledge Graphs for enhanced capabilities in smart manufacturing and Industry 5.0 applications.
Overview of LLM-based agents, including single-agent and multi-agent systems, core components (planning, memory, tool use), and common coordination and planning patterns.
An in-depth exploration of preference alignment techniques for LLMs, including Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).
This article compares monolithic data lake architecture with the decentralized data mesh approach. While data lakes centralize data for easier access, they face scalability challenges. Data mesh treats data as a product owned by domain teams, enhancing agility through four key principles: domain-oriented ownership, data as a product, self-serve infrastructure, and federated governance.
This article explores the world of Big Data, covering core concepts like the 4Vs (Volume, Velocity, Variety, Veracity), key technologies including Hadoop, Kafka, and Spark, and modern architectures such as Persistent Staging Areas and real-time processing systems. It provides a comprehensive overview of the technologies that emerged to address the challenges of modern data growth beyond traditional database capabilities.
This article discusses data integration, focusing on combining data from multiple sources into a unified view for better analysis. It contrasts ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) methodologies, explaining their approaches to data handling. The article also highlights three types of ETL pushdown techniques and the concept of data orchestrators.
This article explores data loading methodologies, including batch and streaming approaches, and various loading strategies such as full and incremental loads. It also examines Change Data Capture (CDC) techniques and the layered architecture of modern data warehouses, from raw data ingestion to presentation marts.