Elasticsearch (7.5, 7.12.0) Logstash & Kibana Stack Tutorial (Udemy.com)
Elasticsearch Logstash Kibana and Beats tutorial with DSL Queries, Aggregator & Tokenizer
Created by: Arun Singh
Last updated July 2021
What you will learn
- Elasticsearch 7.5, update to 7.12.0
- Apache and Nginx log import via Logstash and Beats
- Kibana Visualization
- Winlogbeat
- Metricbeat
- MySql and Oracle Data Import into file and Elasticsearch
- Data extract from Restfull webservice and push into Elasticsearch and text file
- CRUD operations into Elasticsearch via Rest client like POSTMAN
- DSL Query and Aggregation
- Tokenizer
- Learn Searching in Different Languages like English and Hindi
How it compares
Against the other 43 rated Udemy courses in Elastic Search on CourseDuck:
- Rating3.6 out of 5higher than 13% of them
- Reviews33#37 of 44 by number of reviews
- Length3.3 hoursshorter than 60% of them
- Last updatedJuly 2021more recently than 58% of them
Course Description
This Course is divided into Eight Major sections
--For first time user (basic)--
1) Author Introduction and Course Description
2) Introduction of complete ELK stack and different types of beats with internals of Elasticsearch and Lucene Indexing.
-- Usage of Elasticsearch
-- Usage of Logstash and FileBeat and when to use what
-- Usage of Kibana
-- And most interesting, Internals of Elasticsearch Indexing.
3) Installation of Elastic search and Kibana on Windows server. Also upgrade to Elasticsearch 7.12.0 while selecting from archival versions.
(Future Plan): Installation of Elastic search and Kibana on Linux Server
For Advance User
4) Data Ingestion from Mysql, Oracle, Apache, Rest API, & Nginx logs using Logstash & Filebeat with live examples.
5) Kibana for data visualization and dashboard (creation,monitoring & sharing) + Metricbeat + WinlogBeat (Installation, Data Ingestion and Dashboard Management)
6) DSL, Aggregation and Tokenizer Queries- with creating tokens for analyzing English and Hindi Language
-- When querying NoSql DB Elasticsearch what all means and methods to have via DSL.
How if you want to prioritize on any condition like distance from search ?
-- Inbuild functions via Aggregation
-- Searching in Different Languages via tokenizers.
In English you have, "How are you? Is it all good. Looking little upset today"
But in Hindi same will be, "आप कैसे हैं? क्या यह सब अच्छा है। आज थोड़ा परेशान दिख रहे हैं"
so the whole logic comes in tokenization to identify potential diffeences.
Future Plan
7) Cluster Setting
8) Role Security and Monitoring
Instructor Details
- 3.6 Rating
33 Reviews
Arun Singh
Design and Developed secured server architecture to deliver static content from Nginx and NodeJS servers with marketplace api getting delivered with Spring Boot Micro-services with Oracle and NoSql at backend.
Designed ELK Stack and Streaming servers like Kafka for production use, in a direction to achieve highly secured and scalable solutions.
Also help clients & cross platform teams in selecting the best suiting solution.
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Reviews
By Yolanda Castillo on 1/4/2022
me hubieran gustando archivos con los queries
By Marcio Tadeu da Silva on 5/28/2021
This content was very interest.
By Michał Bereziuk Flex VAT: 593-22-05-967 on 11/27/2020
I don't understand all of what he speaks.
By Sreekanth Dindukurthi Reddy on 10/2/2020
Some places poor video clarity. Explained predefined queries. Expecting to Hands on HTTP queries.
By Andrew Griffin on 6/4/2020
Audio and video production quality was rather poor, some of the content was very good, but the query section should really be its own, larger course
By Aditya Verma on 5/28/2020
I really enjoyed the online course. According to me the course is well planned and properly discussed. the way in which content was delivered was very good.
By Prachi Devi on 4/18/2020
Thanks Arun for creating an exceptional course with live examples of Metric Beat, Winlog Beat and DSL query.
By Rajat Kumar on 4/17/2020
Your content is very informative and nicely explained.
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 72 / 100






