Overview
Elastic search is a distributed search and analytics engine that is based in Apache Lucene. It can store data and search it in real-time. There is also the facility of Elasticsearch or Elastic cloud.
Users can use this solution to search and analyze the trends in structured as well as unstructured data. Its key features comprise automatic node recovery, data indexing, index lifecycle management, audit logging, and alert notifications.
Screenshot
Features
- Data Visualization
- Predictive Analytics
- No-Code
- Templates
- API Keys Management
- Analytics/Reporting
- Custom Dashboards
- Indexing
- Multi-Stack Monitoring
- No-Code Sandbox
- Relational Display
- Templates
- Global Language Support
- Typo Tolerance
- Faceted Search
- Natural Language
- Personalization
Specifications
- Deployment: Cloud Based, Web Based, SaaS Based
Training
- Live Online
- Webinars
- Documentation
Elasticsearch Users
Available Support
- Phone
- Live Support
Language Support
- English
Company Details
Company Name: Elastic
Headquarter: United States
Full Address:
Reviews
It is just the best back end for search engines in the market. A NoSQL database that is trustworthy. Also, it is open source. Incredibly easy to use.
Scale: You can run this from a single server or even co-installed on a database or file server. I wouldn’t recommend it, but let’s just say it will run in a small box. On the top side, Elasticsearch will run in clusters managing multiple Terabytes of data. Features: Excellent flexibility to absorb multiple types of data sources, and great integration with Logstash and Kibana.
The fact that Elasticsearch offers really efficient & quick querying of data without a compromise on the different range of queries it can support is really awesome, also Elasticsearch can rank matching documents based on matching criteria which is also very useful. Elasticsearch also handles distributed queries very efficiently.
It’s really easy to set up and there are not many configurations need to get started with. It comes with other supporting tools such as Filebeats to collect the logs from the files, Logstash to ship data to Elasticsearch, and Kibana to visualize the data. It processes millions of data within seconds. Elasticsearch can be clustered with multiple nodes and it guarantees higher data availability. Elasticsearch has lots of proper documentation and community support. It’s easy to integrate with programming languages such as Java
Setting up ES is quick and easy for experimentation with Docker. Creating an index on ES is a seamless app experience. Due to the large user base, building frontend apps with ES integration was easy to hire for.
There’s nothing to dislike. Elasticsearch can be extended in many different ways, and some of them, like adding images to search, will require learning how to properly tag content and allocating time to add those tags.
