Using Elasticsearch and Kibana
4.4 (82 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,551 students enrolled

Using Elasticsearch and Kibana

Scalable Search and Analytics for Document Data
4.4 (82 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,551 students enrolled
Created by Loony Corn
Last updated 1/2020
English
English [Auto]
Current price: $69.99 Original price: $99.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 6 hours on-demand video
  • 1 article
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Construct robust, scalable search for production use in web and enterprise apps
  • Query ES using the ES Domain Specific Language
  • Perform aggregations to extract insights and run analytics on ES
  • Interface with ES using Python
Course content
Expand all 59 lectures 06:08:32
+ Introducing Elasticsearch
12 lectures 01:13:27
Course Materials
00:02
A Brief History of Search
07:51
Steps in Search
08:14
Inverted Index
06:12
Using the Inverted Index
05:19
Lucene
07:20
Elasticsearch Introduced
05:37
Installing ES
08:43
Clusters and Nodes
05:43
Indices and Documents
08:26
Cluster Health
07:00
+ CRUD Operations in Elasticsearch
10 lectures 01:07:20
Curl
07:20
Create Index
08:15
Create Document
08:20
Retrieve Documents
05:23
Update Documents
08:18
Script Elements
04:40
Delete
04:34
mGet
04:39
Bulk Loading
06:45
+ The Query DSL (Domain-Specific Language)
16 lectures 01:41:49
Random Data Gen
05:19
Contexts
05:52
Contexts
05:56
Query Params
07:15
Request Body
09:03
Source Filtering
08:32
Full Text Search_Match
04:10
Full Text Search_MatchPhrasePrefix
07:14
Relevance
08:10
TfIdf
06:06
Common Terms
06:17
Boolean Compound Queries
06:42
Term Queries Boosting Terms
04:42
Filters
06:01
Wildcards
06:09
+ Aggregations
7 lectures 43:54
Types Of Aggregations
03:59
Metric Aggregations
07:12
Cardinality Aggregations
09:07
Bucketing Aggregations
05:31
Bucketing Aggregations_2
06:09
Multilevel Nested Aggregations
05:13
FilterBucketAggs
06:43
+ Elasticsearch and Python
4 lectures 23:17
Pythonsetup
08:32
Create Index
04:58
Documents
05:07
Search_Count
04:40
+ Kibana
9 lectures 56:22
Kibana_elk
04:26
Kibana_Install
02:48
Mapping
07:51
Loading Logs
06:37
Discovery
06:49
Visualize
07:00
Timelion
08:01
Dashboard
03:50
Anaconda and Pip
09:00
Requirements
  • A basic understanding of HTTP and JSON (Javascript Object Notation)
  • Python is helpful for the portions of the course that deal with the ES Python client
Description

Elasticsearch wears two hats: It is both a powerful search engine built atop Apache Lucene, as well as a serious data warehousing/BI technology.

This course will help you use the power of ES in both contexts

ES as search engine technology:

  • How search works, and the role that inverted indices and relevance scoring play
  • The tf-idf algorithm and the intuition behind term frequency, inverse document frequency and field length
  • Horizontal scaling using sharding and replication
  • Powerful querying functionality including a query-DSL
  • Using REST APIs - from browser as well as from cURL

ES as data warehouse/OLAP technology:

  • Kibana for exploring data and finding insights
  • Support for CRUD operations - Create, Retrieve, Update and Delete
  • Aggregations - metrics, bucketing and nested aggs
  • Python client usage


Who this course is for:
  • Developers looking to add robust enterprise search functionality
  • Business analysts looking to use ES and Kibana for business intelligence
  • Data professionals looking to use the ElasticSearch search engine