Udemy – Using Elasticsearch and Kibana
English | Size: 2.75 GB
Category: Tutorial
What Will I 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
Curriculum For This Course
Collapse All 59 Lectures
06:10:53
You, This Course, and Us
02:23
You, This Course, and Us
Preview
02:23
–
Introducing Elasticsearch
01:13:27
Course Outline
Preview
03:00
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
01:09:41
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
The Bulk API
Preview
09:06
Bulk Loading
09:06
–
The Query DSL (Domain-Specific Language)
01:41:49
Search Recap
Preview
04:21
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
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
23:17
Pythonsetup
08:32
Create Index
04:58
Documents
05:07
Search_Count
04:40
–
Kibana
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 is the target audience?
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
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