ELK Stack

An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers: - Quick overview of the Elastic stack - indexation - Analysers - Relevance score - One use case of elasticsearch The query used for the Kibana demonstration can be found here: https://github.com/melvynator/elasticsearch_presentation

Log Management Log Monitoring Log Analysis Need for Log Analysis Problem with Log Analysis Some of Log Management Tool What is ELK Stack ELK Stack Working Beats Different Types of Server Logs Example of Winlog beat, Packetbeat, Apache2 and Nginx Server log analysis Mimikatz Malicious File Detection using ELK Practical Setup Conclusion

This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. It describes the author's experience using Splunk and alternatives like Graylog and Elasticsearch before settling on the ELK stack. The key components - Logstash for input, Elasticsearch for storage and searching, and Kibana for the user interface - are explained. Troubleshooting tips are provided around checking that the components are running and communicating properly.

Log Analytics with ELK Stack describes optimizing an ELK stack implementation for a mobile gaming company to reduce costs and scale data ingestion. Key optimizations included moving to spot instances, separating logs into different indexes based on type and retention needs, tuning Elasticsearch and Logstash configurations, and implementing a hot-warm architecture across different EBS volume types. These changes reduced overall costs by an estimated 80% while maintaining high availability and scalability.

Elastic Agent is a single, unified way to add monitoring to systems and services through integrations. It is managed through Fleet, which provides a centralized UI for defining Elastic Agent policies that specify which integrations to run on which hosts. Fleet Server connects Elastic Agents to Fleet and handles distributing policies and collecting states. The Elastic Package Registry hosts integrations that can be used by Elastic Agent.

ELK (Elasticsearch, Logstash, Kibana) is an open source toolset for centralized logging, where Logstash collects, parses, and filters logs, Elasticsearch stores and indexes logs for search, and Kibana visualizes logs. Logstash processes logs through an input, filter, output pipeline using plugins. It can interpret various log formats and event types. Elasticsearch allows real-time search and scaling through replication/sharding. Kibana provides browser-based dashboards and visualization of Elasticsearch query results.

( ELK Stack Training - https://www.edureka.co/elk-stack-trai... ) This Edureka Elasticsearch Tutorial will help you in understanding the fundamentals of Elasticsearch along with its practical usage and help you in building a strong foundation in ELK Stack. This video helps you to learn following topics: 1. What Is Elasticsearch? 2. Why Elasticsearch? 3. Elasticsearch Advantages 4. Elasticsearch Installation 5. API Conventions 6. Elasticsearch Query DSL 7. Mapping 8. Analysis 9 Modules

The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.

ELK is a stack consisting of the open source tools Elasticsearch, Logstash, and Kibana. Elasticsearch provides a distributed, multitenant-capable full-text search engine. Logstash is used to collect, process, and forward events and log messages. Kibana provides visualization capabilities on top of Elasticsearch. The document discusses how each tool in the ELK stack works and can be configured using inputs, filters, and outputs in Logstash or through the Elasticsearch REST API. It also provides examples of using ELK for log collection, processing, and visualization.

The document discusses the ELK stack, which consists of Elasticsearch, Logstash, and Kibana. It describes some common logging problems companies face and how ELK can help centralize, index, archive, and analyze logs. It provides an example Logstash configuration to collect Apache access logs and MySQL logs, parse them, and output the data to Elasticsearch. It also discusses how to build a custom Logstash filter for Magento logs and load them into Elasticsearch for analysis and visualization in Kibana. The document concludes that while ELK has advantages like being open source and supporting many log formats, its many components require frequent upgrades and out-of-the-box configurations may not be suitable for medium/large deployments

This slide deck talks about Elasticsearch and its features. When you talk about ELK stack it just means you are talking about Elasticsearch, Logstash, and Kibana. But when you talk about Elastic stack, other components such as Beats, X-Pack are also included with it. what is the ELK Stack? ELK vs Elastic stack What is Elasticsearch used for? How does Elasticsearch work? What is an Elasticsearch index? Shards Replicas Nodes Clusters What programming languages does Elasticsearch support? Amazon Elasticsearch, its use cases and benefits

This document provides an overview of OpenSearch, including what it is, its benefits and uses, how to use it, and its key features. OpenSearch is an open source search and analytics engine. It was created as a fork of Elasticsearch 7.10.2 and is powered by the Apache Lucene library. The document discusses how to migrate from Elasticsearch to OpenSearch using various approaches like snapshots, rolling upgrades, or full cluster restarts. It also covers OpenSearch concepts like clusters, nodes, mappings, aggregations, data streams, and plugins.