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ELK Stack (Elastic Stack)

( Duration: 8 Days )

The Elastic Stack (formerly known as the ELK Stack) is a powerful set of open-source tools for managing and making sense of complex data. Using the Elastic stack, you more easily search, visualize, and process data, as well as centralize logging data to quickly root out issues. The ELK Stack is a combination of three open-source products - Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server-side data processing pipeline that inputs data from various sources at the same time, transforms it and sends it to a stash. Kibana enables users to visualise data with graphs and charts in Elasticsearch. The Elastic Stack (ELK Stack) training course provides a good understanding of Elasticsearch, Logstash and Kibana. You will learn about Elasticsearch queries such as Boolean Operators, Fields, Ranges and URI Search. You will also gain knowledge on ELK elasticity and use cases. By the end of the course, you will understand how to use Elasticsearch, Logstash and Kibana, and how it can be used in business.

By attending Elastic Stack (ELK Stack) workshop, delegates will learn to:

  • Get started with Elastic Stack components
  • Perform useful search and analytics queries in Elasticsearch
  • Query and aggregate Elasticsearch indexed data with Query DSL
  • Perform more complex searches using Kibana with data indexed in Elasticsearch
  • Ingest different types of machine data in both Logstash and Beats
  • Work with search results to create powerful visualizations with Kibana
  • Secure and monitor your Elastic Stack
  • Extend your Elastic Stack deployment to a production environment

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

  • SQL
  • JSON Data Format
  • Restful API

This Elastic Stack (ELK Stack) class is intended for:

  • Full Stack Technical Architects
  • Big Data Analytics Engineers - Elastic Stack
  • System Log Analysts
  • Web Analysts
  • Web Administrators



Introduction to ELK Stack

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

Introduction to Logstash

  • 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

Introduction to Elastic Stack

  • 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 Stack
  • Installation and running process

The ElasticSearch Data Model

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

Indexing and Searching in Depth

  • 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

ElasticSearch Mapping

  • 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

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


  • 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

Boolean logic queries

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

Introduction to Data Modeling

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


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

ElasticSearch Admin

  • Monitoring ElasticSearch
  • Production deployment set up
  • Taking a snapshot
  • Backing up
  • Restoring from a snapshot
  • Admin tools
  • Determining the number of shards you need
  • Using new Indices to scale
  • Hardware Selection guidelines
  • Using X-Pack for Monitoring
  • Handling Failover and Rolling Restarts
  • Setting up and using Amazon Elasticsearch Service

Logstash and FileBeats

  • Logstash Introduction
  • Beats introduction
  • Installing and configuring Logstash
  • Using Kibana to visualize log data

Application logging

  • Setting up application logs
  • FileBeats or Java Logback

Working with Alerts

  • Alerting with Watcher
  • Set up Watcher
  • Setting up Alerts

Introduction to Kibana

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


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

Kibana visualization Redux

  • 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

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 Stack

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