This comprehensive 2025 guide compares ELK, EFK, and Loki across architecture, deployment complexity, storage cost, query performance, feature completeness, high‑availability, and real‑world case studies, helping teams of any size choose the most cost‑effective and operationally suitable log collection stack.
This comprehensive guide walks you through building a scalable EFK log management solution, covering architecture components, high‑availability design, environment preparation, detailed Elasticsearch, Fluentd and Kibana deployment steps, index optimization, monitoring, alerting, security hardening, troubleshooting and best‑practice recommendations for modern cloud‑native operations.
This in‑depth technical guide compares ELK, EFK, and Loki across cost, performance, deployment complexity, feature completeness, and suitability for small‑to‑large teams, providing real‑world case studies, decision trees, migration steps, and cost‑optimization tips to help you choose the most efficient logging stack for your organization.
This comprehensive guide explains why log management is critical for large Kubernetes clusters, outlines common pain points, presents full‑stack architectures, details EFK and Loki implementations with code samples, and offers performance, security, cost‑optimization, and future‑trend recommendations.
This comprehensive guide explains the EFK (Elasticsearch, Fluentd, Kibana) log management stack, covering its components, architecture, deployment steps, log collection strategies, index optimization, monitoring, security hardening, troubleshooting and best‑practice recommendations for building a reliable, scalable logging solution in modern cloud‑native environments.
This tutorial walks you through preparing a CentOS 7 environment, installing and configuring Elasticsearch, Kibana, and Filebeat on single‑node servers, adjusting JVM settings, starting services, disabling firewalls, verifying the stack with curl, and setting up Kibana index patterns for log visualization.
This article compares the classic ELK stack, the container‑friendly EFK stack, and the emerging PLG stack (Promtail + Loki + Grafana), outlining their components, architecture, and trade‑offs for log collection, storage, and visualization in Kubernetes deployments.
This article introduces three major log management stacks—ELK, EFK, and PLG (Promtail + Loki + Grafana)—explaining their components, deployment scenarios, and provides a detailed comparison of their storage, indexing, and visualization approaches, especially in Kubernetes environments.
The article explains the components and workflows of ELK, EFK, and PLG (Promtail + Loki + Grafana) logging stacks, compares their architectures, and highlights the trade‑offs between Elasticsearch‑based solutions and Loki‑based solutions for observability in cloud‑native environments.
This article compares popular log aggregation stacks—ELK, EFK, and the PLG combination of Promtail, Loki, and Grafana—detailing their components, architecture, and trade‑offs for cloud‑native environments such as Kubernetes.
Vipshop’s Dragonfly log platform evolved from a costly 260‑node Elasticsearch cluster to a ClickHouse‑based architecture that uses a unified JSON format, vfilebeat ingestion, Flink parsing, and MergeTree storage to achieve high‑throughput writes, fast vectorized queries, flexible TTL management, and dramatically lower operational expenses.
This article explains how to collect, process, and visualize Flink job logs on Kubernetes using an EFK stack with Fluentd, covering logging architectures, deployment of Elasticsearch, Kibana, and Fluentd, and the backend logic for querying and displaying logs in a feature platform.
This guide explains how to prepare resources, create a local‑volume storage class, install the Elasticsearch and Cluster Logging operators, and configure a persistent EFK stack on OpenShift, including YAML definitions, command‑line steps, and best‑practice notes on node selectors and tolerations.
