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

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