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Elasticsearch was released in 2010 which is an open-source and distributed search and analytic engine which is based on Apache Lucene. Elasticsearch works on all types of data that includes text data, numeric data, geospatial, structured and unstructured data. Elastic is well known for its rich and simple REST API, speed, scalability and distributed nature. Elastic stack has a central part known as elasticsearch. It allows you to use tools for data ingestion, data analysis, enrichment, storage and visualization. Elastic stack uses lightweight agents known as beats that transfer the data to the elasticsearch.
Elasticsearch is an open-source, RESTful analytic search engine that allows you to search, store, and analyze large documents providing real-time, scalable search. It allows many companies to solve complex problems thus they are integrating elastic search in their backend infrastructure. You can use for below instances-
Yes, Elasticsearch can also be considered as the No-SQL database. Like MySQL, SQL Server, elasticsearch is also a database engine and database management system. But elasticsearch does not support relational DBMS. If you consider the high-level, elasticsearch also allows you to store information and to retrieve data based on queries which provide results which proves Elasticsearch can act as the database. Among the ACID properties of the database, Elasticsearch only fulfills atomicity, consistent and durable so you can safely call Elasticsearch as a database.
Elasticsearch is a JSON document store that is based on the Apache Lucene search engine. Lucene allows you to do indexing of the document based on set rules. Indexes allow you to map the field values to the document for faster search that can be done by Elasticsearch. Instead of searching the complete document, the system will search the value in the internal index and will let you know which document contains it allowing for faster search. Elasticsearch allows you to tokenize the text in flexible and extensible ways that makes your search even faster than other search engines and MySQL.
Google has integrated Elasticsearch services on the Google cloud platform since 2017. It allows users to deploy the Elasticsearch, Kibana latest versions and their expanding features that provide you with the solution for logging, monitoring, geospatial analysis. After the integration, users are also able to use the benefits of Google Cloud platform like encryption and instance configuration. This integration has increased the users experience more natively to GCP customers via Google Cloud console. It provides the users with a new way to experience the GCP services along with the Elasticsearch services that help with flexible billing, payment and procurement options.
Yes, Elasticsearch is open-source and freely available to only some extent. You are allowed to run the Elasticsearch on-premises, on Amazon EC2. For the users with Amazon free tier can use up to 750 hours per month. It is based on pay for what you use. You will be charged for instance hours, EBS storage and data transfer.
Storing the data in the Elasticsearch is easy but may cost you a lot. Database storage is expensive here. If we consider working on AWS and producing 1 TB of log data. In order to store this raw data, we will be requiring 2 instances of EC2 with each 4TB of EBS storage which will together cost approximately $1500 per month to store 1 TB of Elasticsearch data. But in big companies, 1TB of data is nothing and data may exceed this which will increase the Elasticsearch cost. If you are thinking of replicating the data it will cost you a lot.
Follow below steps to download Elasticsearch-
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