ELK is an acronym of Elasticsearch, Logstash, and Kibana. Each of these is nothing, but an open-source project.

This training program also explains the best possible methods of using the Elastic Search, Logstash, and Kibana with other products and separately.

ELK STACK Hands on and interactive training

Students are encouraged to follow along in the Kibana developer tools to try out queries as the instructor goes through the material. The course is very hands-on and interactive. There are also plenty of hands-on labs.
Introduction to ELK Stack

• Introduction to Logstash
• Introduction to Elastic Search
• Searching in Depth
• Dealing with Human Languages
• Aggregation
• Introduction to Data Modeling
• Geo-locations
• Introduction to Kibana
• Discovering the Data in Depth and Dashboard Analysis

Tools and Plugins

• Head
• X-pack
• Kopf
• Sense
• Cygwin
• Postman
• Bigdesk
• Grafana
• Kibi
• Beats
• Curl

DAY 1

Elasticsearch & Kibana on Kubernetes with Elastic Cloud on Kubernetes

1. Step 1 — Creating a Namespace
2. Step 2 — Creating the Elasticsearch StatefulSet
3. Step 3 — Creating the Kibana Deployment and Service
4. Step 4 — Creating the Fluentd DaemonSet
5. Step 5 (Optional) — Testing Container Logging

1. Introduction to ELK Stack

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

2. Introduction to Logstash (with Hands on)

• • 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

3. Introduction to Elastic Search(with Hands on)

• • Then inverted index
  • Lucene internals
  • Indexes and Documents
  • Shards
  • Cluster Structure – Nodes
  • Data Replication – Replicas and synchronization
  • Pipelining and batching
  • Distributing documents across nodes
  • An overview of Elastic Search
  • Installation and running process

The ElasticSearch Data Model:
Data Model and ElasticSearch API Introduction
Key/Value access
Lists
Objects
Numeric types
Keywords
Text

Day 2

4. Indexing and Searching in Depth (with Hands on)

• • Creating an index
  • Adding documents – Adding Documents to an Index
  • Basic CRUD on a document – Get a documents by ID
  • Modifying – Overwrite a documents, Updating documents, Upserts
  • Get a whole and partial Documents
  • Batch processing – Performing Bulk Operations on Documents
  • Bulk Indexing of Documents from a JSON File
  • Importing test data with cURL
  • Deleting Documents and Indices
  • Organized Search
  • Full-text Search
  • Intricate Search
  • Phrase Search
  • Underlining the Search
  • Multi-field Search
  • Proximity Matching
  • Partial Matching

5. ElasticSearch Mapping (with Hands on)

• • ElasticSearch mapping – schema of a document
  • What is Dynamic mapping?
  • Field data types
  • Adding a mappings to existing indices
  • Updating an existing mappings
  • Parameters of mappings (parameters, custom dates)
  • Adding multi-fields mappings

6. Dealing with Human Languages

• • 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

DAY 3

7. Aggregation (with Hands on)

• • 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
  • Aggregations Types
  • Using Metric Aggregations
  • Cardinality Aggregation
  • Bucketing Aggregations – Introduction to bucket aggregations
  • Filter and Filters Bucketing Aggregations – Defining bucket rules with filters
  • Nested Aggregations and aggregating nested objects
  • Document count approximations
  • Range aggregations
  • Creating histograms

8. Boolean logic queries (with Hands on)

• • Using Boolean Logic with Queries
  • Compound queries
  • Using named queries for development
  • Understanding the match query

9. Introduction to Data Modeling

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

10. Geo-locations (with Hands on)

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

DAY 4

11. Introduction to Kibana. (with Hands on)

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

12. Kibana

• • Kibana introduction
  • Using Kibana to discover
  • Using Kibana to visualize data introduction
  • Kibana and aggregations
  • Creating dashboards with Kibana

13. Kibana visualization Redux (Optional)

• • Line chart visualization
  • Data table visualization
  • Area chart visualization
  • Using Markdown
  • Pie chart and bar chart visualization
  • Other Kibana visualizations
  • Kibana plugins – heatmap, tagcloud
  • Other Kibana plugins

14. Discovering the Data in Depth and Dashboard Analysis

• • 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 StackExploring the live data with the ELK Stack
  • b. How to configure mail a frequently use report from Kibana as per
    schedule
• 3. How to update Kibana/Elastic search field data type after first
  configuration with/without server restart
• 4. Need to cover Kibana Visualization feature in depth
• 5. Also have FAQ covering issues like
  • c. Kibana is hung or ELK is hung – quick analysis and fix
  • d. Out of heap memory issue – optimum configuration

DAY 5

1. creating reports out of multiple indexes. (like combining two indexes to generate meaningful reports)
2. Creating calculated fields in Kibana for reporting.. (the report will be based on the calculated field, instead of one of the field in the document)
3. Injection of CSV files into kibana for visualization. (data input will come from csv file )
4. Can we generate reports out of the data from multiple sources. (example: elastic search and csv file)