Data Visualization: Kibana - Elastic Search (Research Proposal Sample)

Elasticsearch
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Course
Institutional Affiliation
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Kibana; Elastic Search
It is a component of Elastic Stack which turns data into visual insights which can be used for decision making. Its interactive data visualization components entail maps, charts and histograms which goes beyond establishing a standard dashboard used for analysis and visualization ("View monitoring data in Kibana | Kibana Guide [7.15] | Elastic", 2018). It combines sources from different sources to determine correlations, analyze trends to determine hidden relationships. It used data structure known as inverted index which supports full-text search which is very first.
The data stored in Elasticsearch is categorized into two categories; content and time series data such as log entries (Zamfir et al, 2019). The data stored is managed through indexed and fully searchable real-time. To enhance data management, Elasticsearch enables definition of multiple tiers of data nodes of different features, it automatically transitions indices based on individual performance desires using the index lifecycle management (ILM), it performs asynchronous searches of data, and finally, it leverages searchable snapshots stored remotely (Zamfir et al, 2019).
Elasticsearch does not require a dedicated field data type for tables. By default, any field can contain zero or more values, but all values ​​in the array must be of the same field type.
Most Elasticsearch data types are available in Elasticsearch SQL (Zamfir et al, 2019). With the exception of the date data type which is the date and time in Elasticsearch SQL, Elasticsearch SQL maps all Elasticsearch data types to data types with the same name.