- Learning ELK Stack
- Table of Contents
- Learning ELK Stack
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Introduction to ELK Stack
- The need for log analysis
- Challenges in log analysis
- The ELK Stack
- ELK data pipeline
- ELK Stack installation
- Installing Elasticsearch
- Running Elasticsearch
- Elasticsearch configuration
- Elasticsearch plugins
- Installing Logstash
- Running Logstash
- Logstash with file input
- Logstash with Elasticsearch output
- Configuring Logstash
- Installing Logstash forwarder
- Logstash plugins
- Installing Kibana
- Configuring Kibana
- Running Kibana
- Kibana interface
- Summary
- 2. Building Your First Data Pipeline with ELK
- 3. Collect, Parse and Transform Data with Logstash
- Configuring Logstash
- Logstash plugins
- Summary
- 4. Creating Custom Logstash Plugins
- 5. Why Do We Need Elasticsearch in ELK?
- 6. Finding Insights with Kibana
- 7. Kibana – Visualization and Dashboard
- 8. Putting It All Together
- 9. ELK Stack in Production
- 10. Expanding Horizons with ELK
- Index
A typical ELK stack data pipeline looks something like this:
In a typical ELK Stack data pipeline, logs from multiple application servers are shipped through Logstash shipper to a centralized Logstash indexer. The Logstash indexer will output data to an Elasticsearch cluster, which will be queried by Kibana to display great visualizations and build dashboards over the log data.
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