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Talend Data Integration Basics
February 26, 2018
MongoDB developer cum Administrator
February 26, 2018
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ElasticSearch Stack

$650.00 $600.00

Course Details – Elasticsearch is a greatly scalable analytics engine and open-source full-text search. It confess to search, analyze, store big volumes of rapidly and in near real time. It is normally used as basic
engine/technology that function applications that have complex search requirements and features. It
confesses you to start with one machine and scale to hundreds. The latest version using in the market
is Elasticsearch 5.2.0.

Why to attend Agilitics Pte. Ltd. Training ?

Classes are conducted by Certified Elasticsearch Working Professionals with 100 % Quality
Assurance.

With an experienced Certified practitioner who will teach you the essentials you need to know to kickstart
your career on Elasticsearch. Our training make you more productive with your Elasticsearch Training Online. Our training style is entirely hands-on. We will provide access to our desktop screen and will be actively conducting hands-on labs with real-time projects.

Description

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

Elasticsearch Basics
ElasticSearch is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead.

This is like retrieving pages in a book related to a keyword by scanning the index at the back of a book, as opposed to searching every word of every page of the book.

This type of index is called an inverted index, because it inverts a page-centric data structure (page- >words) to a keyword-centric data structure (word->pages).

ElasticSearch uses Apache Lucene to create and manage this inverted index.

In ElasticSearch, a Document is the unit of search and index.

An index consists of one or more Documents, and a Document consists of one or more Fields.

In database terminology, a Document corresponds to a table row, and a Field corresponds to a table column.

Schema
Unlike Solr, ElasticSearch is schema-free. Well, kinda.

Whilst you are not required to specify a schema before indexing documents, it is necessary to add mapping declarations if you require anything but the most basic fields and operations.

This is no different from specifying a schema!

The schema declares:

  • what fields there are
  • which field should be used as the unique/primary key
  • which fields are required
  • how to index and search each field

In ElasticSearch, an index may store documents of different “mapping types”. You can associate multiple mapping definitions for each mapping type. A mapping type is a way of separating the documents in an index into logical groups.

To create a mapping, you will need the Put Mapping API, or you can add multiple mappings when you create an index.

Query DSL
The Query DSL is ElasticSearch’s way of making Lucene’s query syntax accessible to users, allowing complex queries to be composed using a JSON syntax.

Like Lucene, there are basic queries such as term or prefix queries and also compound queries like the boolquery.

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