Elastic Stack Introduction

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

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

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

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

Centralized Logging System using ELK Stack The document discusses setting up a centralized logging system (CLS) using the ELK stack. The ELK stack consists of Logstash to capture and filter logs, Elasticsearch to index and store logs, and Kibana to visualize logs. Logstash agents on each server ship logs to Logstash, which filters and sends logs to Elasticsearch for indexing. Kibana queries Elasticsearch and presents logs through interactive dashboards. A CLS provides benefits like log analysis, auditing, compliance, and a single point of control. The ELK stack is an open-source solution that is scalable, customizable, and integrates with other tools.

Elasticsearch is a distributed, open source search and analytics engine that allows full-text searches of structured and unstructured data. It is built on top of Apache Lucene and uses JSON documents. Elasticsearch can index, search, and analyze big volumes of data in near real-time. It is horizontally scalable, fault tolerant, and easy to deploy and administer.

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.

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 Stack Training - https://www.edureka.co/elk-stack-trai... ) This Kibana tutorial by Edureka will give you an introduction to the Kibana 5 Dashboard and help you get started with working on the ELK Stack. Below are the topics covered in this Kibana tutorial video: 1. Introduction To ELK Stack 2. Role Of Kibana In ELK 3. Kibana 5 Dashboard 4. Demo: Kibana For Visualization & Analytics

Kibana is a data visualization tool that is part of the ELK stack (Elasticsearch, Logstash, Kibana) and allows users to search, analyze, and visualize data stored in Elasticsearch. The document discusses Kibana's essential features including Discover to query data, Visualize to create visualizations, and Dashboard to combine them. It also covers additional tools like Dev Tools, X-Pack plugins, and Machine Learning capabilities.

So, what is the ELK Stack? "ELK" is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

This document discusses Elasticsearch, an open source search engine that can handle large volumes of data in real time. It is based on Apache Lucene, a full-text search engine, and was developed by Shay Banon in 2010. Elasticsearch stores data in JSON documents and works by indexing these documents so they can be quickly searched. Some key advantages include being RESTful, scalable, simple and transparent, and fast. Disadvantages include only supporting JSON for requests and responses as well as some challenges around processing. The document recommends starting with the official Elasticsearch documentation.

The document discusses Netflix's use of Elasticsearch for querying log events. It describes how Netflix evolved from storing logs in files to using Elasticsearch to enable interactive exploration of billions of log events. It also summarizes some of Netflix's best practices for running Elasticsearch at scale, such as automatic sharding and replication, flexible schemas, and extensive monitoring.

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.

The document provides an introduction to the ELK stack for log analysis and visualization. It discusses why large data tools are needed for network traffic and log analysis. It then describes the components of the ELK stack - Elasticsearch for storage and search, Logstash for data collection and parsing, and Kibana for visualization. Several use cases are presented, including how Cisco and Yale use the ELK stack for security monitoring and analyzing biomedical research data.