Kibana Tutorial : A Complete guide for the beginners

Thursday, December 26, 2019

Kibana Tutorial : A Complete guide for the beginners



Kibana is an open source data visualisation dashboard for Elasticsearch. It provides visualisation capabilities on top of the content indexed on an Elasticsearch Cluster. User can create bar, line and scatter plots or pie charts and maps on the top of large volumes of data.
Kibana
Kibana


1. Overview of Kibana


Kibana also provides presentation tool, referred to as Canvas, that allows users to create slide decks that pull live data directly from Elasticsearch.

The Combination of Elasticsearch, Kibana and Logstash is known as ELK Stack.

Developed by - Elastic NV 


2. What is Kibana ?

Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, maps diagrams.

3. Installation of Kibana ?

For installation of Kibana please visit the below link - 


For more details and practical view please watch below video - 



4. Indexing data into Kibana ?

While indexing data into Elasticsearch, we need to run Kibana as well so, data will be indexed to Elasticsearch and Kibana.

Software Link -
Download Node JS - https://nodejs.org/en/download/ for MAC/Windows/Linux/UbuntuDownload Code Editor - Visual Studio - https://visualstudio.microsoft.com/

Project Link - 



More details and code explanation's please watch below video


                                    


5. How to perform GET, POST, PUT, DELETE Methods from Kibana ?


Software Link -
Download Node JS - https://nodejs.org/en/download/ for MAC/Windows/Linux/UbuntuDownload Code Editor - Visual Studio - https://visualstudio.microsoft.com/


Projects Link - 

git clone https://atique1224@bitbucket.org/atique1224/youtube_kibana_tutorial.git


More details and practical example please watch below video -





6. Elasticsearch Aggregation and Projection from Kibana ?


Elasticsearch Aggregation - 


# Aggregation with sum query

GET /students/_search

{
  "query": {
        "match_all": {}
  },
  "aggs" : {
        "Casual_Leaves" : { "sum" : { "field" : "leaves.CL" } 
        }
  }
}

# Aggregation with count query get no of repeated value
GET /students/_search
{
  "query": {
        "match_all": {}
  },
  "aggs" : {
      "count": {
            "terms": {
                  "field": "dept.keyword",
                  "size": 100,
                  "order": {
                  "_key": "desc"
                  }
            }
}
  }
}


#get the total count of record
GET /students/_count
{
  
}


Elasticsearch Projection -




#Its a sort of Projects


GET /students/_search
{
    "_source": ["student_id","skills"],
   "query": {
        "match_all": {}
    }
}

More details and explanation please watch below video -



7. Elasticsearch Pagination and Scroll Query from Kibana ?


Elasticsearch pagination - 

   So Say for example you have an huge record is there into your elasticsearch might be more than 10+ millons, If you want to display the entire records to the front end in a single shot, as we know that is not possible. So to prevent this kind of problem Elasticsearch supports 2 solutions first one is pagination.
Image result for pagination

Elasticsearch Scroll - 

   Second Solutions is the Elasticsearch Scroll. Each and every time it will create a scroll id and also you can set time, like that perticular scroll id will expires after what time.


More details and practical example please watch the below video - 



8. How to visualized the Elasticsearch data in Kibana ?

For visualize the Elasticsearch data we need to make sure that data has to be present into kibana. Before visulize the data we need to create an index pattern of the particular indices.
Also we have to make sure data has to be 4c quality of data. What is 4c Quality ?

Correctness - Validate data accuracy through comparison to external reference.

Currency - Deliver new and updated content in a timely manner.

Completeness - Provide the right data attributes and analysis to ensure customers have all of the necessary information to make critical decisions.

Consistency - Standardize identifiers and content across databases and products to be sure customers receive consistent information regardless of product platform.

More details and practical example please visit below video link - 














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