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Elasticsearch

overview

Elasticsearch is a search server based on Lucene. It provides a distributed, multitenant-capable full-text search engine with a RESTful web interface and schema-free JSON documents. Elasticsearch is developed in Java and is released as open source under the terms of the Apache License. Elasticsearch is the second most popular enterprise search engine.
prerequisties
  • Exposure to or interest in Elasticsearch, relational databases, distributed systems, or information retrieval.
Duration
Online
  • It is a 08 days program and extends up to 2hrs each.
  • The format is 20% theory, 80% Hands-on.

Corporate
  • It is a 2 days program and extends up to 8hrs each.
  • The format is 20% theory, 80% Hands-on.
Classroom
    Private Classroom arranged on request and minimum attendies for batch is 4.
course content
  • Introduction
    • Terminology, basic concepts, implementation, setup, and basic operations
    • What is Elasticsearch?
    • Overview of best practices
    • What's in a distribution?
    • Understanding Elasticsearch cluster, shards, and replicas
    • Discussion of configuration, APIs, and local gateway
  • Multi-Tenancy
    • Value of multiple indices, index aliases, and cross-index operations
    • Introduction to data flow
  • Elasticsearch Index
    • In-depth analysis of mappings, indexing, and operations
    • Discussion of transaction logs and Lucene indexing
    • Understanding configuration options, mappings, APIs, and available settings
  • Search
    • Understanding search Query DSL
    • In-depth understanding of search components: aggregations, search types,highlighting and other options.
    • Overview of bitSets, filters and Lucene
  • Advanced Search and Mapping
    • Introduction to aggregations and nested document relations
    • Understanding nested objects and parent-child relationships
    • The importance of geolocation, mapping, indexing query percolation,relevancy, searching, and more
  • Advanced Distributed Model
    • Cluster state recovery, low level replication, low level recovery, and shard allocation
    • How to approach data architecture
    • Index templates, features, and functionality
  • Big Data Design Pattern
    • In-depth content on multiple indices, overallocation, shard overallocation,node types, routing, replication, and aliases
  • Preparing for Production
    • Discussion on capacity planning and data flow
    • Performance tuning, more on data flow, and memory allocation.
  • Running in Production
    • Installation, configuration, memory file descriptions, and hardware Monitoring, alerts, thread pools, information and stats APIs
Videos
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