Elastic Stack

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leoElastic Stack is a group of open source products from Elastic designed to help users take data from any type of source and in any format and search, analyze, and visualize that data in real time. The product group was formerly known as ELK Stack, in which the letters in the name stood for the products in the group: Elasticsearch, Logstash and Kibana. A fourth product, Beats, was subsequently added to the stack, rendering the potential acronym unpronounceable. Elastic Stack can be deployed on premises or made available as Software as a Service (SaaS).


Use Cases for Elastic Stack

  • Logging and Log Analysis

            For anyone familiar with Elasticsearch, this one should be no surprise. The ecosystem built up around                              Elasticsearch has made it one of the easiest to implement and scale logging solutions. Many of the the users                on our platform are no different and have taken advantage of this to either add logging to their main use case,                or are using us purely for logging. From Beats, to Logstash, to Ingest Nodes, Elasticsearch gives you plenty of                options for grabbing data wherever it lives and getting 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. A cool example of this that you can                check out is our Twitter connector, which allows you to set up hashtags to watch on Twitter and then grab all                  tweets with those hashtags and analyze them in Kibana. We built that product on core Elastic Stack                                  components and added some additional pieces to help it scale.


  • Full Text Search

            It’s also no surprise that full text search, as the core capability of Elasticsearch, is high on this list. The                            surprising part is the applications of this among our customer set, which go well beyond traditional Enterprise                search or E-commerce. From fraud detection/security to collaboration and beyond, our users have shown that              Elasticsearch’s search capabilities are powerful, flexible, and include a great number of tools to make 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 easily grab data for common applications. Whatever                        technologies you use, there’s a pretty good chance that 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 really easy.

  • Visualizing Data

            With tons of charting options, a tile service for geo-data, and TimeLion for time-series data, Kibana is an                          amazingly powerful and easy to use visualization tool. For every use case above there is some visual                                component handled by Kibana. Once you’re comfortable with the various data ingest tools, you’ll find that                        Elasticsearch + Kibana will become your go-to tool for visualizing data that you’re trying to wrap your head                      around.

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