ELK Training Overview
ELK Stack is designed to allow users to take to data from any source, in any format, and to search, analyze, and visualize that data in real-time. ELK provides centralized logging that is useful when attempting to identify problems with servers or applications. It allows you to search all your logs in a single place.
ELK Training Objective
- Ingesting Data usually of all shapes, sizes, and sources
- Parsing and Transforming Data in Logstash is on a large scale- Elasticsearch Corporate course
- Large Variety of Output being defined for the Logstash Training for better performance
ELK Training Audience
Software professionals who want to learn the basics of Logstash and its programming concepts in simple and easy steps. It describes the components and functions of Logstash with suitable examples.
ELK Training Prerequisites
Basic understanding of Ruby, JSON, and web technologies. Additionally, it will be helpful for the readers to be familiar with Logging Techniques and Regex patterns.
ELK Training Outline
○ Lucene
○ Plugins
○ Configuration files
○ CRUD
○ Sorting and Relevance
○ Topology and Clusters
○ Distributed Document Store
○ Full-Body Search
○ Index Management
○ Bulk Operations
○ Sharding
○ Lab
○ Requirements
○ Different ways of installing Elasticsearch
○ Configuration Files
○ Web interface
○ Lab
○ Mappings
○ CRUD and relationship to documents/indices
○ Data Types
○ Dynamic Field Mappings
○ Index Templates
○ Lab
○ Structured Search
○ Full-Text Search
○ Analyzers – Tokenizers and Filters
○ Character Filters
○ Testing Analyzers
○ Built-In Analyzers
○ Synonym Handling
○ Multi Field Search
○ Proximity Matching
○ Partial Matching
○ Relevance Adjustment
○ Lab
○ Distributed Search Fundamentals
○ Query DSL Deep Dive
○ Query Advice and Best Practices
○ Lab
○ Human Language Processing
○ Language Configuration
○ Chinese characters configuration (optional if needed)
○ Tokenization
○ Normalization
○ Finding root of the words
○ Stopwords
○ Synonyms
○ Misspellings and Typos
○ Lab
○ Terms, Phrase, Completion, and Context
○ Suggestors
○ Best Practices
○ Lab
○ Aggregations
○ Fundamentals
○ Deep dive of each aggregation
○ Lab
○ Nested Objects and Documents
○ Impact of document structure on search
○ Lab
○ Relationships
○ Geolocation
○ Aggregation
○ Lab
○ Monitoring
○ Deployment
○ Maintenance and basic optimization
○ Lab
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