Module 1
Introduction to Elasticsearch
The Story of Elasticsearch
The Components of Elasticsearch
Installation and configuration
Getting started
Documents
Indexes
Indexing Data
Searching Data
The Bulk API
Hands-on Lab:Index a dataset, then search the data
The Search API:
Introduction to the Search API
URI Searches
Request Body Searches
The match Query
The match_phrase Query
The range Query
The bool Query
Module 2
Hands-on Lab(45 minutes):Write various queries that search documents
Text Analysis:
What is Analysis?
Building an Inverted Index
Analyzers
Custom Analyzers
Character Filters
Tokenizers
Token Filters
Defining Analyzers
Synonyms
How to Choose an Analyzer
Segments
Hands-on Lab:Perform the steps for configuring text analysis in Elasticsearch; use the Analyze API to
see how the built-in analyzers work; define custom analyzers by configuring character filters,
tokenizers and token filter
Mappings:
What is a Mapping?
Dynamic Mappings
Module 3
Defining Explicit Mappings
Adding Fields
Drive Deeper into Mappings
Specifying Analyzers
Dynamic Templates
Index Templates
Hands-on Lab: Define a custom mapping for a new index; use an index template to customize a mapping
More Search Features:
The Anatomy of a Search
Term-based Queries
Filters
The match_phrase_prefix Query
The multi_match_Query
Fuzziness
Highlighting
The Distributes Model
Starting a Node
Creating an Index
Starting a Second Node
Shards: Distribution of an Index
Module 4
Distributing Documents
Replication
Split Brain
Other Node Types
Development vs. Production Mode
Hands-on Lab(30 minutes): Startup a multi-node cluster, see how documents indexed into Elasticsearch are
not immediately available for searching
Working with Search Results:
Relevance
Boosting Relevance
DFS Query-then-fetch
Sorting Results
Doc Values and Fielddata
Pagination
Scroll Searches
Choosing a Search Type
Hands-on Lab(30 minutes): Run queries that involve controlling the results of searches using relevance
boosting, sorting and pagination
Aggregations:
What are Aggregations?
Module 5
Types of Aggregations
Buckets and Metrics
Common Metrics Aggregations
The range Aggregation
The data_range Aggregation
The terms Aggregation
Nesting Buckets
Global Aggregation
The missing Aggregation
Histograms
Data Histograms
Percentiles
Top Hits
Significant Terms
Sorting Buckets
Hands-on Lab: Perform various advanced bucket and metrics aggregations on the stocks index
Handling Relationships:
The Need for Data Modeling
The Need for Nested Types
Nested Types
Querying a Nested Type
Module 6
Sorting on a Nested Type
The Nested Aggregation
Parent/Child Types
The has_child Querry
The has_parent Querry
Hands-on Lab: Define and use a nested mapping type and a parent/child mapping type
Logstash
Overview of Logstash
Configuration
Transport and processing inputs, filters, and outputs
Events:Structure and options
Hands-on lab with a use case of any log data present as CSV
Kibana 5
Installing and Configuring Kibana with elasticsearch
Understanding using queries, single and multiquery
Search criteria and filters
Elasticsearch aggregation
Kibana index setup for analysis
Kibana Discover interface
Kibana Visualization interface
Module 7
Visulization of each type of graph present in kibana such as Pie
Chart,Bar graph, Line graph, Geolocation graph, Metrics graph etc
Constructing Simple & Complex
Visualizations using dasnboard
Beats and X-Pack
File beats, Winlog
Security
Monitoring
Reporting
Big data with ELK stack