Elasticsearch Training
Learn How to Build Distributed Search Engine Solutions with Elasticsearch
THE GOAL OF COURSE
The purpose of the Elasticsearch Training Program is to enable participants to consolidate their knowledge in the their minds with experience sharing, example implementations and case studies, while providing detailed information on key concepts and techniques on Elasticsearch
WHAT YOU WILL LEARN
- Learn the terminology and basic concepts behind Elasticsearch Architecture and its components, selection criteria and best solution designs,
- Understand how you should prepare your Elasticsearch solution to production,
- Specialize in the most appropriate cluster configurations and integrations with the hardware and systems within the data center,
- Learn Mappings, Analyzers, Query, Filter and Aggregation Concepts in detail,
- Develop technical knowledge and competencies with workshops,
- Make a significant contribution to your development in Scalable Search Solutions,
- Be able to find answers to your questions at the points where your head is installed while developing after the program
WHO BENEFITS
- Employees who are doing or planning to make a career in the Big Data place
- Leading teams in technology / IT functions
- Leaders responsible for Digital Transformation.
- Data Scientists and Data Analysts
- New Graduates and University Students
Developing Highly Scalable and Customizable Search Systems Will Not Be Hard for You After This Program
Theories and practices you will see throughout the program will answer your questions on Elasticsearch, also you will learn key techniques to develop the right strategy while implementing your projects
COURSE OUTLINE
- Module-1: Introduction to Elasticsearch
- The History of Elasticsearch
- Why Elasticsearch
- Topology and Clusters
- Elasticsearch Installation and Configurations
- Index and Document CRUD Operations
- Bulk Operations
- Analyzers – Tokenizers and Filters
- Built-In Analyzers
- Testing Analyzers
- Lab: Workshop on Index CRUD Operations
- Module-2: Deep Dive to Mappings
- Index and Document Mapping Operations
- Data Types
- Dynamic Field Mappings
- Index Templates
- Updating Mappings
- Lab: Examples with Index and Document Mapping Operations
- Module-3 Understanding Queries and Filters
- Understanding Query Concept
- Distributed Search Fundamentals
- Deep Dive to Query DSL
- Query Advice and Best Practices
- Lab: Query and Filtering Workshop Studies
- Module-4 Working With Suggestions
- Exploring Built-in Suggestions
- Terms, Phrase, Completion, and Context Suggestors
- Using Terms Suggesters
- Using Phrase Suggesters
- Using Completion Suggesters
- Using Context Suggesters
- Building Alternative Suggesters
- Module: Aggregations
- Best Practices
- Lab: Examples on Suggestions
- Module-5 Document Modelling
- Flat Document Models
- Inner Nested Document Models
- The Effect of Modeling on Search Queries
- Suggestions on Modeling
- Relevancy and Scoring Concepts
- Understanding Percolator and Notification
- Module-6 Performance Optimization and Best Practices
- Building Different Architectures for Different Needs
- Optimizations on Operating Systems
- Production Setup and Performance Tips
- Case Studies
- Visualizing Search Results with Kibana
- Lab: You will learn common Elasticsearch operations by applying lots of examples on the sample datasets