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Top 5 Elasticsearch Use Cases

Other than "You Know, for Search", the uses of Elasticsearch continue to grow and change over time. We at ObjectRocket have been offering hosted Elasticsearch on the ObjectRocket platform for a while now and have been able to see some clear trends among our customers and how they're using the product. Below are the top 5 uses cases that we see on the platform:

#1 - 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.

#2 - 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.

#3 - 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.

#4 - 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.

#5 - 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.

Conclusion

Though that's not every use case, those are the heavy-hitters we see on our service. Elasticsearch and the rest of the Elastic Stack have proven to be extremely versatile, and as you can see above, there are multiple ways to integrate Elasticsearch into what you're doing today and gain extra insight. That to me is the coolest part of Elasticsearch, the ability to enhance the technologies you're already using rather than just another database to store your data.

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