What are common use cases for Elastic Stack+ Platform

What are common use cases for Elastic Stack+ Platform

Example use cases for Elastic Stack+ Platform

Logging and Log Analysis
The ecosystem built up around Elasticsearch has made it one of the easiest to implement and scale logging solutions. From Beats to Logstash to Ingest Nodes, Elasticsearch gives you plenty of options to grab data wherever it lives and get it indexed. From there, tools like Kibana give you the ability to create rich dashboards and analysis, while Curator allows you to put the retention period on autopilot.

Scraping and Combining Public Data
Like log data, the Elastic Stack has plenty of tools to make grabbing and indexing remote data easy. Also, like most document stores, the lack of a strict schema gives Elasticsearch the flexibility to take in multiple different sources of data and still keep it all manageable and searchable. 

Full-Text Search
Full-text search is the core capability of Elasticsearch. Full-text search can be used from fraud detection/security to collaboration and beyond, as Elasticsearch’s search capabilities are powerful, flexible, and include a great number of tools to make the search easier; Elasticsearch has its own query DSL as well as built-in capabilities for auto-complete, “Did you mean” responses and more.

Event Data and Metrics
Elasticsearch also operates really well on time-series data like metrics and application events. This is another area where the huge Beats ecosystem allows you to grab data for common applications easily. Elasticsearch has the components to grab metrics and events out of the box… and in the rare case that it can’t, adding that capability is straightforward.

Visualizing Data
With tons of charting options, a tile service for geo-data, and TimeLion for time-series data, Kibana is a potent and easy-to-use visualization tool. For every use case above, there is some visual component handled by Kibana. 



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