Elasticsearch is known to be highly scalable as well as an open-source or full-text search as well as an analytics engine. It permits the user to store, search, as well as analyze the volume that is big coded data quickly and in real-time. It is used generally as an engine that is underlying as well as technology that is empowering applications that have complex search features as well as requirements. The Elasticsearch is capable of providing the users as well as the developers a system which is distributed right on top of the standard analyzer of Lucene for the process of indexing as well as automatic type guessing at the same time utilizing the feature in the JSON. Setting up is very easy since it is capable of shipping with sensible defaults but at the same time, it has a short curve for the process of learning to grasp the basics along with a bit of effort it can become very productive as well as very quick. It is described to be schema-less, and have some default in the data and index. Elasticsearch considers to be a living heart what's the most popular analytics platforms and the role is to become synonymous with the name of itself in the stack. Today it is one of the most popular and well-known database systems available. It was initially released in the year 2010 and it has completely open-source and to the surprise of the users and the Developers, it is a NoSQL database. This means that it is capable of storing data in an unstructured manner and the user is not able to use SQL to query it. The configuration of the Elasticsearch is done with the use of the configuration file and the location of this file depends highly on the user's operating system. In the file, the user can configure settings that are general like node name and so on as well as network settings when the data is stored, the memory, log files, and so on. For the process of development as well as testing purposes the settings that are default will suffice but at the same time, it is recommended that the user does some research about what is the setting he has to use manually and define it before going into the production process. The Elasticsearch has the ability not to run automatically after the process of installation and the user has to manually start the operation. It depends on the user-specific system on how Elasticsearch will run. Once the user indexes the data into the Elasticsearch then he can start the process of searching as well as analyzing it easily and flexibly. The simplest query that the user can do is to fetch single items.
BI & Data Visualization
It provides the user to connect from the reporting as well as business intelligence tools to the information of the Elasticsearch.
ETL & Replication
It can simplify the process of extracting transforming and loading along with the data replication process in the warehouse of RDBMS.
In the process of data management, the work from the data management application is straight with the use of the Elasticsearch data.
Workflow & Automation
It can integrate Elasticsearch the other program of automation as well as workflow.
Data visualization is described to be a tool that presents a modern approach to the integration of the data for all the users and Developers operating with it. It is not like the solution of extracting transforming and loading which replicates data. The process of data virtualization leaves the data in the system source. It can deliver the users with integrated information in real-time as well as applications that are used by the business owners. It can provide the users a secure layer to catalog me to centralized, search, discover, as well as governing these data which is unified along with its relationships. Data visualization is known to be able to integrate data across the system of the enterprise irrespective of the data format as well as location. It also provides the user with an approach to perform the process of accessing, managing as well as delivering data without the need to replicate it.