Master Modern IT Operations: Skill Maps, ELK Architectures & Big Data Monitoring

1. Development and Trends of Operations Positions

Operations is a comprehensive technical role that integrates multiple disciplines such as networking, systems, development, security, application architecture, and storage.

From early network management to today's system, network, security, and development‑focused operations engineers, the division of labor has become increasingly granular, demanding higher overall skill levels.

The future trend for operations is “high, precise, cutting‑edge”: professionals must stand at a high technical level, master specific skills, and stay abreast of frontier technologies.

2. System Operations Skill Map

System operations form the foundation of all ops work. Strengthening these basics enables deeper learning of subsequent specialized skills.

The diagram below lists the essential competencies for system operations:

3. Web Operations Skill Map

Web operations is the most common and well‑paid ops role, requiring a broad knowledge base while retaining legacy skills.

The following diagram outlines the necessary web‑ops competencies:

4. Big Data Operations Skill Map

Since 2017, big data has permeated daily life and continues to grow, especially with strong governmental support, making big‑data ops a front‑line skill set.

The diagram below presents the core competencies for big‑data operations:

5. Container Operations Skill Map

Container technology sparked a revolution around 2015‑2016 and has since become mainstream across Chinese enterprises, with a thriving ecosystem of vendors, open‑source communities, and public clouds.

In 2019, container adoption continued to accelerate; the diagram below shows the essential container‑ops skills:

6. Data Is King

Just as a skyscraper’s stability depends on a solid foundation, operations data is the foundation of effective ops management. It includes CMDB, logs, production databases, and knowledge bases.

Log data is especially critical: it provides a comprehensive view of system or device behavior, enables root‑cause analysis, and can predict potential failures, such as security incidents reflected in security logs.

7. Log Data Processing

Operations must collect, filter, analyze, and visualize massive log volumes. Tools like the ELK stack (Elasticsearch, Logstash, Kibana) simplify real‑time log ingestion, analysis, and graphical presentation.

Different ELK architectures suit varying log volumes.

8. ELK Architecture 1

Logstash agents on each node collect and filter logs, then forward them to a central Elasticsearch cluster for storage. Kibana provides a web UI for querying and reporting.

This setup is simple and easy to start, but Logstash consumes significant CPU and memory, and the lack of a message‑queue buffer introduces a risk of data loss. It is best suited for beginners or low‑volume environments.

9. ELK Architecture 2

This variant introduces a message‑queue (e.g., Kafka or Redis). Logstash agents (first‑level) send data to the queue; a second‑level Logstash pulls, filters, and forwards the data to Elasticsearch. Kibana visualizes the results.

This design balances network traffic and prevents data loss if the Logstash server fails, making it suitable for medium‑sized clusters. However, both Logstash and Elasticsearch bear heavy loads, often requiring clustering to distribute the burden.

10. ELK Architecture 3

Building on Architecture 2, this model replaces the Logstash agents with Filebeat, uses a Kafka cluster for queuing, and runs both Logstash and Elasticsearch in clustered mode.

This setup is ideal for large clusters and massive data volumes: Filebeat reduces the resource impact on production systems, Kafka ensures reliable data transport, and clustered Logstash/Elasticsearch improve scalability, throughput, and fault tolerance.

11. Applying Big‑Data Thinking to Operations Monitoring

Log analysis originated in operations and has expanded to broader business insights, revealing immense value in operational data.

A big‑data‑driven monitoring approach provides a platform that empowers ops teams to solve problems, rather than forcing big‑data platforms to address issues.

A typical big‑data ops architecture includes data collection, filtering, storage, and visualization layers.

Obtain required data Filter anomalies and set alert thresholds Trigger alerts via third‑party monitoring platforms

Logs remain the most reliable source for assessing system health. Historically, engineers manually inspected logs after incidents; now, integrated platforms allow predefined log‑analysis logic to automate detection and response.

Source: https://blog.51cto.com/51ctoblog/2449687