• An overview of ELK Stack
• Why choose ELK?
• Architecture of ELK
• An explanation of Elastic Search Logstash and Kibana

• A brief explanation of Logstash
• Installation process
• Log file configuration
• Stashing process of the first event
• Analyzing logs with Logstash
• Uses of input and output
• Plugins
• Execution model

• An overview of Elastic Search
• Installation and running process
• Indexing documents list
• Saving the documents
• Searching the documents

• Organized Search
• Full-text Search
• Intricate Search
• Phrase Search
• Underlining the Search
• Multi-field Search
• Proximity Matching
• Partial Matching

• An introduction to various human languages
• Identifying Words
• Controlling Tokens
• Decreasing Words to their actual Root Form
• Stop words: Performance versus Precision
• Synonyms
• Typographical Errors and Spelling Mistakes

• An insight into concepts
• A brief introduction to Aggregation
• Analysis process
• Filtering Process of the Aggregations and Queries
• Sorting Multivalue Loads
• Expected Aggregation
• Doc Values and Field Data

• Elastic Search versus RDBMS
• Relationships handling
• Nested objects
• Scale Designing

• Major Geo Points
• Geo Hashes
• Geo Aggregations
• Geo Shapes

• An overview of Kibana
• Installation process of Kibana
• Sample data loading process
• Discovering the saved data
• Visualization of the data
• Working with the Dashboard

• Set-up of Time Filter
• Searching of the saved data
• Filtering by the Field
• Viewing the document data
• Viewing the document context
• Viewing the field statistics
• Data visualization
• Dashboard analysis
• Exploring the live data with the ELK Stack

To master the ELK Stack concepts, a candidate must-have the basic understanding of the following:

• SQL
• JSON Data Format
• Restful API