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Workday HCM Demo New Batches Starting from Wednesday... 26-07-2017
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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.
  • Exposure to or interest in Elasticsearch, relational databases, distributed systems, or information retrieval.
  • It is a 08 days program and extends up to 2hrs each.
  • The format is 20% theory, 80% Hands-on.

  • It is a 2 days program and extends up to 8hrs each.
  • The format is 20% theory, 80% Hands-on.
    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
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