Sharing cutting‑edge big data and AI technologies, with occasional lifestyle insights.
The article outlines the full lifecycle of data warehouse modeling—from business requirements and system architecture considerations through conceptual, logical, and physical design, illustrated with a procurement bidding case study and a comparison of MPP and Hadoop solutions.
The article explains why unstructured data—documents, images, audio, and video—now dominates enterprise information, outlines five key management challenges such as data variety, information silos, and weak governance, and then presents a four‑step governance framework and ECM‑based solution backed by industry standards and Gartner insights.
This article introduces Byte's open‑source Coze Loop and Coze Studio platforms, detailing their Apache‑2.0‑licensed architectures, full lifecycle management for AI agents, core features such as prompt engineering, evaluation, observation, and a low‑code visual development environment built with Golang, React, and TypeScript.
The article explains how to construct a data metric system by first defining metric categories and naming conventions, then distinguishing atomic and derived metrics, and clarifying related concepts such as business processes, modifiers, time periods, dimensions, and business domains.
The article explains digital transformation as a systemic, data‑driven shift that leverages cloud, big data, IoT, AI and blockchain to optimise value systems, build new capabilities, and redesign enterprise architecture through five coordinated tasks and three categories of measurable benefits.
This article provides a detailed technical comparison of Apache Hudi, Delta Lake, and Apache Iceberg, covering core features such as incremental pipelines, concurrency control, merge‑on‑read, partition evolution, multi‑mode indexing, ingestion tools, and real‑world use cases, and explains why Hudi often leads for heavy update workloads.
This article walks through the fundamentals of Prometheus—its origins, core features, installation methods, configuration file structure, PromQL basics, HTTP API, Grafana integration, exporters for various services, and alerting with Alertmanager—providing step‑by‑step commands and examples to set up a complete monitoring solution.
This article walks through assembling a monitoring stack—JMeter → InfluxDB → Grafana and node_exporter → Prometheus → Grafana—detailing data collection, backend listener configuration, storage structures, Grafana dashboard setup, and example performance test visualizations.
