Prerequisites
To apply for the ELK STACK Training, you need to either:
- To learn big data Analytics tools you need to know at least one programming language like Java, Python or R.
- You must also have basic knowledge on databases like SQL to retrieve and manipulate data.
- You need to have knowledge on basic statistics like progression, distribution, etc. and mathematical skills like linear algebra and calculus.
Course Curriculum
Module 1: Introduction to ELK Stack
This section will introduce you to the core concepts and techniques used for developing it. You will learn about ELK architecture and how it would be helpful for organizations.
Topics:
- Overview of ELK Stack
- Architecture of ELK
- Why ELK?
- Detailed overview of ElasticSearch, Kibana, and Logstash
Learning outcome: Upon completion of this chapter you will gain a complete understanding of ELK Stack architecture, familiarity with terminology, basics of ElasticSearch, Kibana, and Logstash, and usage of ELK Stack in companies.
Module 2: Elasticsearch CRUD API
- Creating Index
- On the Fly Index Creation
- List down the indexes in Elasticsearch
- Anatomy of a document
- Create a document in Elasticsearch
- Searching documents from Elasticsearch
- Deleting documents in Elasticsearch
- Update documents in Elasticsearch
Module 3: Advanced CRUD API
- Handling concurrency in Elasticsearch
- Introduction of programming concepts
- Connecting the Elasticsearch through using programming interface •
- Bulk Request
- Bulk Indexing
- Bulk Retrieval
- Strategery followed while using Bulk Indexing
Module 4: Multi-Node Cluster Setup
- Different types of node
- Configuring the Nodes – Window/Linux
Module 5: Parsing with Logstash
In this section you will learn the basics of Logstash, the procedure to install Logstash, creating an advanced pipeline, and the process to stitch various input and output plugins to unify data from diversified sources.
Topics:
- Introduction to Logstash
- Installation of Logstash
- Stashing an event
- Configuring a log file
- Parsing Logs using Logstash
- Execution Model
- Plugins
- Stitching together various input and output data.
Learning outcome: After completion of this chapter, you will have gained hands-on experience in the areas such as Logstash installation process, execution of an event, creation of advanced pipeline, unifying data gathered from various sources, etc.
Module 6: Searching with ElasticSearch
This section has been designed to provide you with a deep understanding of the ElasticSearch component.
Topics:
- ElasticSearch Overview
- Installation of Elasticsearch
- Searching a Document
- Indexing Documents
- Retrieving a Document
Learning Outcome: Once this chapter finishes you will be able to retrieve information of an employee, perform a structured search, gain knowledge of full-text search, etc.
Module 7: Dealing with Human Language
This section helps you solve various problems such as singular and plural words, typos, tenses. You will also learn how to deal with human language improving performance.
Topics:
- Getting Started with languages
- Normalizing Tokens
- Identifying Words
- Words reduction to their Root Form
- Performance versus Precision
- Typos and Misspellings
- Synonyms
Learning Outcome: Upon the completion of this training you will be able to improve search performance, look for misspellings or alternate spellings, identifying words, typos and misspellings.
Module 8: Searching in Depth
This section has been designed to provide in-depth knowledge of ElasticSearch. Here you will learn data and run search queries through it and you will learn how to index and query data allowing you to take advantage of partial matching, word proximity, and language awareness.
Topics:
- Structured Search
- Complicated Search
- Full-text Search
- Highlighting our Search
- Phrase Search
- Proximity Matching
- Partial Matching
- Multi-field Search
Learning Outcome: Upon the completion of this section you will be able to Perform Structured Search, full-text search query, multi-field search, associated words, and partial matching query.
Module 9: Data Aggregation
This section is designed to provide you knowledge of data sets and query performance on data sets to get real-time answers.
Topics:
- High-Level Concepts
- Time Analysis
- Introduction to Aggregation
- Filtering Aggregations and Queries
- Approximate Aggregation
- Sorting Multiple Buckets
- Doc Values and Field Data
Learning Outcome: upon the completion of this chapter you will be able to gain complete knowledge of concepts such as buckets and metrics, bar charts using buckets, Date Histogram, Sort multi-value bucket, Filter queries, and aggregation.
Module 10: Data Modeling
It is very difficult to perform the joins between various entities that reside on different hardware. It is very expensive and very challenging. This module teaches you how data modeling is done in ElasticSearch.
Topics:
- Handling Relationships
- ElasticSearch vs RDBMS
- Nested Objects
- Designing for Scale
- Parent-Child Relationship
Learning Outcome: Upon the completion of this section you will be able to get the best search results with Denormalizing Data, use Nested Objects, gain knowledge of Parent-Child Relationship, and knowledge of other data modeling concepts.
Module 11: Geolocation
The great advantage of Elasticsearch is that it allows you to combine geographical location with structured search, full-text search, and analytics. In this chapter, you will gain complete knowledge of how geo-location works for providing the best possible results to the users based on the given criteria.
Topics:
- Geo Point
- Geo Aggregations
- Geohashes
- Geo Shapes
Learning outcome: Upon the completion of this module you will be able to Aggregate Geo Distance, understand different Geo shapes and aggregate geohash grid, and gain the complete knowledge of Geo points.
Module 12: Visualization with Kibana
These modules teach you the process to search, view, interact with data stored in Elasticsearch indices. This allows you to analyze and visualize data using various tables, charts, and maps. This module will help you learn how to visualize data using Kibana.
Topics:
- Introduction to Kibana
- Loading Sample Data
- Installing Kibana
- Visualizing your Data
- Discovering your Data
- Working with Dashboard
Learning Outcome: Upon the completion of this chapter you will gain hands-on experience in the concepts such as Kibana installation, Ingesting.Json files into Elasticsearch, creating visualization in different ways, and dashboard summarization.
Module 13: Implementation of ELK Stack
In this section, you will be learning how to explore data from the Discover page. You can access each document in every index that suits the chosen indexed pattern. Here you can also learn the process to submit the search results, search queries, and view documents. This section provides you with complete knowledge for implementing ELK Stack.
Topics:
- Setting the Time Filter
- Filtering by Field
- Searching your Data
- Viewing Field Statistics
- Viewing Document Data
- Dashboard
- Data Visualization
- Live data analysis with ELK stack
Learning outcome: Upon the successful completion of this chapter you will gain hands-on expertise in the Time Filter, Creating a Dashboard, and Document Context.
